Introduction

The current project was made during and for the course Data Science for Business Analytics teached by Professor Vatter at HEC Lausanne.

To better understand the choice of the topic, it was motivated by a large interest in the real estate market. Sadly, data from the swiss market is quite hard to find and I therefore decided to turn myself to the UK.

One of the main decision point was the discovery of the IMD (Indice of Multiple Deprivation) made by the UK government to rank different regional places in term of Deprivation. Moreover, the HM Land Registry opened their price sales data to the public for non-commercial use, which is perfect in the actual case.

Those discoveries gave me the ability to have multiple data sources letting me work on this topic.

The Research question that will be discussed and answered in this paper are:

Before diving into the project in itself, here is a map of the UK LSOA (Lower super output area) that will be used in the project with their ranking in terms of Deprivation, places ranked low are highly deprived and places ranked high are lowly deprived.

Data

This section is dedicated to present the data sets used and tidy them.

Sources

To make this project, 3 data sets were used:

    1. The priced paid data
    1. The IMD data
    1. The Postcode data

The 2 firsts data sets comes from the UK government online website while the third one comes from a web developer who made it from various sources before all cited at the bottom of the page.

Let’s now have a look at each dataset

Priced Paid Data

We will first start by having a look at the housing price data. As you can see, the first thing that has to be done is the naming of the variables from what we know on the website. After naming we have the following 16 variables:

Now let’s speak about the data points and what we will keep

#> tibble [992,818 x 16] (S3: tbl_df/tbl/data.frame)
#>  $ Unique_transaction_code: chr [1:992818] "{8F1B26BD-60CA-53DB-E0"..
#>  $ Price                  : int [1:992818] 221950 246995 244950 139..
#>  $ Date                   : chr [1:992818] "2019-04-26 00:00" "201"..
#>  $ Postcode               : chr [1:992818] "TS17 5FF" "TS15 9ZH" ""..
#>  $ Housing_type           : chr [1:992818] "D" "D" "T" "S" ...
#>  $ New_or_old             : chr [1:992818] "Y" "Y" "Y" "Y" ...
#>  $ Freehold_Leasehold     : chr [1:992818] "F" "F" "F" "F" ...
#>  $ Street_number          : chr [1:992818] "3" "16" "13" "40" ...
#>  $ NA                     : chr [1:992818] "" "" "" "" ...
#>  $ Street                 : chr [1:992818] "CARRAWBURGH ROAD" "GRE"..
#>  $ Locality               : chr [1:992818] "INGLEBY BARWICK" "" """..
#>  $ Town_City              : chr [1:992818] "STOCKTON-ON-TEES" "YAR"..
#>  $ District               : chr [1:992818] "STOCKTON-ON-TEES" "STO"..
#>  $ County                 : chr [1:992818] "STOCKTON-ON-TEES" "STO"..
#>  $ NA                     : chr [1:992818] "A" "A" "A" "A" ...
#>  $ NA                     : chr [1:992818] "A" "A" "A" "A" ...
#> NULL


This dataset contains 992’818 observations, each one being a sale that occurred in the UK in 2019. As you can see, there are 3 variables that were called NA, this is due to the fact that I was unable during the search of their meaning to find what they were meaning since the first one is empty the whole time and the two others I sadly wasn’t able to fin the answer on their exact meaning for the moment (time of HW4).

We have 16 variables with the most important:

  • Unique_transaction_code Unique code for each sale
  • Date Which is the date of the day the sale occured
  • Postcode Postcode of the place of the building
  • Price Price of the exchange
  • Housing_type Type of housing with D = Detached, S = Semi-Detached, T = Terraced, F = Flats/Maisonettes, O = Other
  • New_or_old Telling us if the construction is new (Y) or old (N)
  • Freehold_Leasehold Specifying if the building was contains the land (Freehold) or not (Leasehold)
  • Street_number Street number
  • Street Name of the street
  • Locality Name of the locality
  • Town_City Name of the city
  • District Name of the district

For the analysis we will only keep the Unique_transaction_code,the Postcode, the Price, the Housing_type, the New_or_old, the Freehold_Leasehold adn the Date. Since those variable will either be needed for the analysis of will give the ability to answer further questions or explanation of the results. While locality and street names would not grant any information to resolve the research questions.


IMD Data

We will now look at the IMD data, this data is for the year 2019 too. Compared to the Priced paid data, this one is already quite usable and do not need as much tidying as the first one, the first thing to do is to rename the variables to present them:

#> tibble [32,844 x 20] (S3: tbl_df/tbl/data.frame)
#>  $ LSOA_code                : chr [1:32844] "E01000001 " "E0100000"..
#>  $ LSOA_name                : chr [1:32844] "City of London 001A ""..
#>  $ Local_Authority_code     : chr [1:32844] "E09000001 " "E0900000"..
#>  $ Local_Authority_Name     : chr [1:32844] "City of London " "Cit"..
#>  $ IMD_rank                 : chr [1:32844] "29 199 " "30 379 " "1"..
#>  $ IMD_decile               : num [1:32844] 9 10 5 3 5 3 2 3 3 3 ...
#>  $ Income_rank              : chr [1:32844] "32 831 " "29 901 " "1"..
#>  $ Income_decile            : num [1:32844] 10 10 6 2 5 2 2 3 3 3 ...
#>  $ Employment_rank          : chr [1:32844] "32 742 " "31 190 " "1"..
#>  $ Employment_decile        : num [1:32844] 10 10 5 3 7 4 2 4 6 5 ...
#>  $ Skill_rank               : chr [1:32844] "32 842 " "32 832 " "2"..
#>  $ Skill_decile             : num [1:32844] 10 10 9 4 6 7 4 6 7 4 ...
#>  $ Health_Deprivation_rank  : chr [1:32844] "32 113 " "29 705 " "1"..
#>  $ Health_Deprivation_decile: num [1:32844] 10 10 6 6 7 5 4 3 5 6 ...
#>  $ Crime_rank               : chr [1:32844] "32 662 " "32 789 " "2"..
#>  $ Crime_decile             : num [1:32844] 10 10 9 10 6 2 4 2 1 3 ..
#>  $ Barrier_to_Housing_Rank  : chr [1:32844] "7 319 " "11 707 " "2 "..
#>  $ Barrier_to_Housing_Decile: num [1:32844] 3 4 1 1 1 1 1 1 1 1 ...
#>  $ Living_Environment_rank  : chr [1:32844] "7 789 " "13 070 " "4 "..
#>  $ Living_Environment_Decile: int [1:32844] 3 4 2 3 4 4 3 3 1 2 ...
#> NULL


This datast contains 32’844 observations for 22 variables:

  • LSOA_code Code of the LSOA (Lower Layer Super Output Area)
  • LSOA_name Name of the LSOA
  • IMD_rank & IMD_decile Ranking and decile of each LSOA for IMD
  • Income_rank & Income_decile Ranking and decile of each LSOA for Income
  • Employment_rank & Employment_decile Ranking and decile of each LSOA for Employment
  • Crime_rank & Crime_decile Ranking and decile of each LSOA for Crime
  • Living_Environment_rank & Living_Environment_Decile Ranking and decile of each LSOA for Living Environment
  • Health_Deprivation_Rank & Health_Deprivation_Decile Ranking and decile of each LSOA for Helth deprivation
  • Skill_rank & Skill_DecileRanking and decile of each LSOA for Skill
  • Barrier_to_Housing_rank & Barrier_to_Housing_Decile Ranking and decile of each LSOA for Barrier to housing
  • Education & Education1 Ranking and decile of each LSOA for Education
  • Local_Authority_Name & Local_Authority_code Name and code of the local authority

For the analysis we will keep the LSOA_code & LSOA_name to be able to do the link with the rest of the data and for answering the research questions, we need to have the rank and decile for IMD , Income, Employment, Crime, Health Deprivation & Skill.

As you can see, we have an issue with the data type of the ranks and decides, we therefore need to change their type as numeric to be able to use them easily which gives us the following:

#> 'data.frame':    32844 obs. of  14 variables:
#>  $ LSOA_code                : chr  "E01000001" "E01000002" "E01000"..
#>  $ LSOA_name                : chr  "City of London 001A " "City of"..
#>  $ IMD_rank                 : int  29199 30379 14915 8678 14486 725..
#>  $ IMD_decile               : int  9 10 5 3 5 3 2 3 3 3 ...
#>  $ Income_rank              : int  32831 29901 18510 6029 14023 626..
#>  $ Income_decile            : int  10 10 6 2 5 2 2 3 3 3 ...
#>  $ Employment_rank          : int  32742 31190 15103 7833 21692 114..
#>  $ Employment_decile        : int  10 10 5 3 7 4 2 4 6 5 ...
#>  $ Crime_rank               : int  32662 32789 29363 31059 18848 49..
#>  $ Crime_decile             : int  10 10 9 10 6 2 4 2 1 3 ...
#>  $ Health_Deprivation_rank  : int  32113 29705 17600 17907 21581 16..
#>  $ Health_Deprivation_decile: int  10 10 6 6 7 5 4 3 5 6 ...
#>  $ Skill_rank               : int  32842 32832 26386 12370 17511 20..
#>  $ Skill_decile             : int  10 10 9 4 6 7 4 6 7 4 ...
#> NULL


Postcodes Data

Finally we have the Postcodes Data, which is there only to give the ability to link the other two tables together.

To gain time, and avoid describing the 2,634,087 observations and 49 variables since only a few will be really interesting, we will go straight to the few we are going to keep.

So here is an insight to the kept table:

#> tibble [2,634,087 x 2] (S3: tbl_df/tbl/data.frame)
#>  $ Postcode : chr [1:2634087] "AB1 0AA" "AB1 0AB" "AB1 0AD" "AB1 0"..
#>  $ LSOA_code: chr [1:2634087] "S01006514" "S01006514" "S01006514" "..
#> NULL


This table, once wrangled contains only the Postcodeand the LSOA code so we are able to link the 2 other one together because Postcodes is a unique value in this table while it could be multiple times in Price paid data and the LSOA code is multiple time in this table and is a unique value in the IMD data.

Joining Data together

Now that we talked about each dataset we can linked them all together for the rest of the project.

We will do this in 2 parts :

    1. Linking the Postcode data to the Price paid data to have the LSOA code in the resulting table
    1. Using the newly added LSOA code to link the IMD table to the Price paid data


We then have a table like the one seen right under, I am only outputting the firsts 1000 rows since 992’818 observations is way too much :

#> tibble [992,818 x 21] (S3: tbl_df/tbl/data.frame)
#>  $ Unique_transaction_code  : chr [1:992818] "{8F1B26BD-60CA-53DB-"..
#>  $ Postcode                 : chr [1:992818] "TS17 5FF" "TS15 9ZH""..
#>  $ Price                    : int [1:992818] 221950 246995 244950 1..
#>  $ Housing_type             : chr [1:992818] "D" "D" "T" "S" ...
#>  $ New_or_old               : chr [1:992818] "Y" "Y" "Y" "Y" ...
#>  $ Freehold_Leasehold       : chr [1:992818] "F" "F" "F" "F" ...
#>  $ Date                     : chr [1:992818] "2019-04-26 00:00" "2"..
#>  $ LSOA_code                : chr [1:992818] "E01033474" "E0101223"..
#>  $ LSOA_name                : chr [1:992818] "Stockton-on-Tees 023"..
#>  $ IMD_rank                 : int [1:992818] 28901 21703 115 115 21..
#>  $ IMD_decile               : int [1:992818] 9 7 1 1 7 1 10 10 4 4 ..
#>  $ Income_rank              : int [1:992818] 30254 30573 67 67 3057..
#>  $ Income_decile            : int [1:992818] 10 10 1 1 10 1 10 10 3..
#>  $ Employment_rank          : int [1:992818] 30001 29614 154 154 29..
#>  $ Employment_decile        : int [1:992818] 10 10 1 1 10 1 10 10 5..
#>  $ Crime_rank               : int [1:992818] 27174 3933 2103 2103 3..
#>  $ Crime_decile             : int [1:992818] 9 2 1 1 2 1 10 10 5 1 ..
#>  $ Health_Deprivation_rank  : int [1:992818] 8264 11514 115 115 115..
#>  $ Health_Deprivation_decile: int [1:992818] 3 4 1 1 4 1 9 9 2 3 ...
#>  $ Skill_rank               : int [1:992818] 28788 27495 138 138 27..
#>  $ Skill_decile             : int [1:992818] 9 9 1 1 9 1 10 10 7 7 ..
#> NULL


But, we have information coming from the whole UK in the sales while the data on the IMD is only about England, we therefore need to filter which leaves us with this amount of rows:

#> [1] 934103


Now let’s have a look at the final table of 934’103 observations and 21 variables which will be saved as “Final_analysis_table.csv” in the data section of the project.

Unique_transaction_code Postcode Price Housing_type New_or_old Freehold_Leasehold Date LSOA_code LSOA_name IMD_rank IMD_decile Income_rank Income_decile Employment_rank Employment_decile Crime_rank Crime_decile Health_Deprivation_rank Health_Deprivation_decile Skill_rank Skill_decile
{8F1B26BD-60CA-53DB-E053-6C04A8C03649} TS17 5FF 221950 D Y F 2019-04-26 00:00 E01033474 Stockton-on-Tees 023F 28901 9 30254 10 30001 10 27174 9 8264 3 28788 9
{8F1B26BD-60CB-53DB-E053-6C04A8C03649} TS15 9ZH 246995 D Y F 2019-03-29 00:00 E01012235 Stockton-on-Tees 024A 21703 7 30573 10 29614 10 3933 2 11514 4 27495 9
{8F1B26BD-60CC-53DB-E053-6C04A8C03649} TS18 2FN 244950 T Y F 2019-05-17 00:00 E01012266 Stockton-on-Tees 014E 115 1 67 1 154 1 2103 1 115 1 138 1
{8F1B26BD-60CD-53DB-E053-6C04A8C03649} TS18 2FN 139950 S Y F 2019-05-31 00:00 E01012266 Stockton-on-Tees 014E 115 1 67 1 154 1 2103 1 115 1 138 1
{8F1B26BD-60CE-53DB-E053-6C04A8C03649} TS15 9FD 271995 D Y F 2019-05-31 00:00 E01012235 Stockton-on-Tees 024A 21703 7 30573 10 29614 10 3933 2 11514 4 27495 9
{8F1B26BD-60CF-53DB-E053-6C04A8C03649} TS18 2FD 84450 T Y F 2019-04-26 00:00 E01012266 Stockton-on-Tees 014E 115 1 67 1 154 1 2103 1 115 1 138 1
{8F1B26BD-60D0-53DB-E053-6C04A8C03649} TS17 5JS 354950 D Y F 2019-04-29 00:00 E01033478 Stockton-on-Tees 023H 31095 10 32203 10 31564 10 31499 10 29176 9 30318 10
{8F1B26BD-60D1-53DB-E053-6C04A8C03649} TS17 5JP 279950 D Y F 2019-05-31 00:00 E01033478 Stockton-on-Tees 023H 31095 10 32203 10 31564 10 31499 10 29176 9 30318 10
{8F1B26BD-60D5-53DB-E053-6C04A8C03649} TS26 0NW 115000 F Y L 2019-05-03 00:00 E01033467 Hartlepool 001G 11517 4 6950 3 15783 5 14069 5 3291 2 20115 7
{8F1B26BD-60D6-53DB-E053-6C04A8C03649} TS8 9GG 145950 T Y F 2019-05-31 00:00 E01032553 Middlesbrough 018H 12878 4 18082 6 11783 4 1366 1 8140 3 21543 7
{8F1B26BD-60D7-53DB-E053-6C04A8C03649} TS17 5FF 158950 S Y F 2019-05-31 00:00 E01033474 Stockton-on-Tees 023F 28901 9 30254 10 30001 10 27174 9 8264 3 28788 9
{8F1B26BD-60D9-53DB-E053-6C04A8C03649} TS18 2FL 133950 T Y F 2019-05-24 00:00 E01012266 Stockton-on-Tees 014E 115 1 67 1 154 1 2103 1 115 1 138 1
{8F1B26BD-60DA-53DB-E053-6C04A8C03649} TS15 9ZF 289995 D Y F 2019-05-24 00:00 E01012235 Stockton-on-Tees 024A 21703 7 30573 10 29614 10 3933 2 11514 4 27495 9
{8F1B26BD-60DC-53DB-E053-6C04A8C03649} TS17 0FU 149950 S Y F 2019-06-17 00:00 E01012285 Stockton-on-Tees 020D 10726 4 9716 3 7030 3 22225 7 7579 3 13507 5
{8F1B26BD-60DD-53DB-E053-6C04A8C03649} TS17 0FU 179950 S Y F 2019-06-14 00:00 E01012285 Stockton-on-Tees 020D 10726 4 9716 3 7030 3 22225 7 7579 3 13507 5
{8F1B26BD-60DE-53DB-E053-6C04A8C03649} TS15 9FL 292950 D Y F 2019-06-20 00:00 E01012303 Stockton-on-Tees 024G 31004 10 26189 8 27374 9 32221 10 21984 7 27587 9
{8F1B26BD-60DF-53DB-E053-6C04A8C03649} TS5 8FD 259995 D Y F 2019-06-19 00:00 E01012022 Middlesbrough 015A 19123 6 18568 6 18023 6 16108 5 8849 3 21353 7
{8F1B26BD-60E0-53DB-E053-6C04A8C03649} TS5 8DW 257950 D Y F 2019-06-14 00:00 E01012022 Middlesbrough 015A 19123 6 18568 6 18023 6 16108 5 8849 3 21353 7
{8F1B26BD-60E1-53DB-E053-6C04A8C03649} TS22 5GX 449950 D Y F 2019-06-19 00:00 E01011959 Hartlepool 014A 22123 7 24700 8 24623 8 9817 3 17385 6 27683 9
{8F1B26BD-60E2-53DB-E053-6C04A8C03649} TS22 5TL 599950 D Y F 2019-06-14 00:00 E01011959 Hartlepool 014A 22123 7 24700 8 24623 8 9817 3 17385 6 27683 9
{8F1B26BD-60E3-53DB-E053-6C04A8C03649} TS17 5JS 409950 D Y F 2019-05-08 00:00 E01033478 Stockton-on-Tees 023H 31095 10 32203 10 31564 10 31499 10 29176 9 30318 10
{8F1B26BD-60E4-53DB-E053-6C04A8C03649} TS13 4RX 51000 T N F 2019-06-13 00:00 E01033473 Redcar and Cleveland 013E 4211 2 2581 1 2518 1 15528 5 4401 2 3024 1
{8F1B26BD-60E5-53DB-E053-6C04A8C03649} TS26 0FN 279995 D Y F 2019-06-07 00:00 E01011982 Hartlepool 014E 28897 9 30081 10 27057 9 21821 7 18171 6 31537 10
{8F1B26BD-60E7-53DB-E053-6C04A8C03649} TS10 5DF 92000 S N F 2019-06-21 00:00 E01012106 Redcar and Cleveland 003C 11896 4 10067 4 7141 3 21377 7 4924 2 15561 5
{8F1B26BD-60E8-53DB-E053-6C04A8C03649} TS8 9FT 234950 D Y F 2019-05-28 00:00 E01032553 Middlesbrough 018H 12878 4 18082 6 11783 4 1366 1 8140 3 21543 7
{8F1B26BD-60E9-53DB-E053-6C04A8C03649} TS5 8EJ 182950 S Y F 2019-01-31 00:00 E01012022 Middlesbrough 015A 19123 6 18568 6 18023 6 16108 5 8849 3 21353 7
{8F1B26BD-60EA-53DB-E053-6C04A8C03649} TS4 2WD 167950 D Y F 2019-06-21 00:00 E01012026 Middlesbrough 006A 8503 3 9049 3 8432 3 2460 1 4845 2 11219 4
{8F1B26BD-60EB-53DB-E053-6C04A8C03649} TS8 9BF 234950 D N F 2019-05-24 00:00 E01012046 Middlesbrough 013A 15731 5 15494 5 9994 4 5756 2 10860 4 24304 8
{8F1B26BD-60EC-53DB-E053-6C04A8C03649} TS7 0RS 394995 D Y F 2019-02-08 00:00 E01032593 Middlesbrough 020F 29410 9 29986 10 24778 8 26731 9 15641 5 30323 10
{8F1B26BD-62DE-53DB-E053-6C04A8C03649} CW10 0DU 140000 T N F 2019-06-28 00:00 E01018419 Cheshire East 025A 27649 9 24574 8 27346 9 16268 5 20323 7 23019 8
{8F1B26BD-62DF-53DB-E053-6C04A8C03649} WA5 1JJ 207000 S N L 2019-07-17 00:00 E01012496 Warrington 016B 24718 8 25771 8 23736 8 22861 7 15861 5 22857 7
{8F1B26BD-62E0-53DB-E053-6C04A8C03649} CH64 4BP 230000 S N F 2019-06-28 00:00 E01018533 Cheshire West and Chester 006B 30812 10 28399 9 23855 8 30125 10 20310 7 30080 10
{8F1B26BD-62E1-53DB-E053-6C04A8C03649} CW5 5QR 158000 S N F 2019-07-19 00:00 E01018454 Cheshire East 047B 9707 3 7947 3 10175 4 13679 5 8709 3 4350 2
{8F1B26BD-62E2-53DB-E053-6C04A8C03649} CH66 8PA 305000 D N F 2019-07-05 00:00 E01018360 Cheshire West and Chester 025A 20685 7 25731 8 24798 8 18552 6 19067 6 24948 8
{8F1B26BD-62E3-53DB-E053-6C04A8C03649} CH3 9AL 275000 S N F 2019-06-14 00:00 E01018371 Cheshire West and Chester 046C 22761 7 19557 6 20922 7 32144 10 24606 8 23089 8
{8F1B26BD-62E4-53DB-E053-6C04A8C03649} ST7 3RX 139000 S N F 2019-07-19 00:00 E01018430 Cheshire East 033F 28965 9 25176 8 23194 8 32215 10 18407 6 21905 7
{8F1B26BD-62E5-53DB-E053-6C04A8C03649} WA16 8DL 329000 S N F 2019-06-28 00:00 E01018610 Cheshire East 009C 18930 6 14790 5 13028 4 18813 6 15722 5 18422 6
{8F1B26BD-62E6-53DB-E053-6C04A8C03649} CW8 4PE 145000 S N F 2019-06-28 00:00 E01018672 Cheshire West and Chester 012B 9687 3 9261 3 7570 3 14358 5 7803 3 6202 2
{8F1B26BD-62E7-53DB-E053-6C04A8C03649} WA6 6SG 235000 D N F 2019-06-24 00:00 E01018690 Cheshire West and Chester 002F 31325 10 30807 10 29891 10 26778 9 28692 9 32024 10
{8F1B26BD-62E8-53DB-E053-6C04A8C03649} CH45 6UL 220000 S N F 2019-06-28 00:00 E01007314 Wirral 004E 12103 4 14998 5 8607 3 11699 4 6892 3 21727 7
{8F1B26BD-62E9-53DB-E053-6C04A8C03649} CH45 4SB 204950 S N F 2019-07-05 00:00 E01007316 Wirral 004F 19939 7 21771 7 16777 6 18968 6 9915 4 24675 8
{8F1B26BD-62EA-53DB-E053-6C04A8C03649} CH66 1NZ 190000 T N F 2019-07-10 00:00 E01018547 Cheshire West and Chester 005B 16053 5 17164 6 9881 4 15185 5 11743 4 12621 4
{8F1B26BD-62EB-53DB-E053-6C04A8C03649} CW9 8AQ 325000 S N F 2019-07-01 00:00 E01018703 Cheshire West and Chester 023F 25855 8 26100 8 18945 6 26564 9 21786 7 30221 10
{8F1B26BD-62EC-53DB-E053-6C04A8C03649} CW11 1GJ 125000 T N F 2019-05-31 00:00 E01018437 Cheshire East 030B 11630 4 11180 4 8050 3 19364 6 5290 2 20428 7
{8F1B26BD-62ED-53DB-E053-6C04A8C03649} CH65 5BH 78000 S N F 2019-06-28 00:00 E01018552 Cheshire West and Chester 011D 8400 3 7878 3 6792 3 20705 7 4535 2 4768 2
{8F1B26BD-62EE-53DB-E053-6C04A8C03649} CW2 6EF 85000 T N F 2019-06-18 00:00 E01018445 Cheshire East 045A 3719 2 3732 2 3740 2 7320 3 3171 1 3942 2
{8F1B26BD-62EF-53DB-E053-6C04A8C03649} SK12 2BB 187500 F N L 2019-06-27 00:00 E01018585 Cheshire East 001A 27192 9 24620 8 20277 7 17882 6 23299 8 27941 9
{8F1B26BD-62F0-53DB-E053-6C04A8C03649} CH44 6QD 74500 T N F 2019-07-15 00:00 E01007278 Wirral 008E 273 1 763 1 555 1 1624 1 142 1 1594 1
{8F1B26BD-62F1-53DB-E053-6C04A8C03649} CW12 4RE 430000 D N F 2019-06-27 00:00 E01018393 Cheshire East 022A 20700 7 29112 9 29505 9 21267 7 29124 9 27174 9
{8F1B26BD-62F2-53DB-E053-6C04A8C03649} CH66 2NQ 293000 D N F 2019-07-03 00:00 E01018556 Cheshire West and Chester 013A 23445 8 21970 7 16772 6 23827 8 18388 6 18543 6
{8F1B26BD-62F3-53DB-E053-6C04A8C03649} CW2 8PP 142000 S N F 2019-07-01 00:00 E01018511 Cheshire East 044B 10090 4 8933 3 8595 3 17547 6 10111 4 7623 3
{8F1B26BD-62F4-53DB-E053-6C04A8C03649} CW2 8NN 150000 S N F 2019-06-26 00:00 E01018512 Cheshire East 044C 10119 4 11018 4 9795 3 11923 4 6613 3 6078 2
{8F1B26BD-62F5-53DB-E053-6C04A8C03649} CW8 1JP 140000 S N F 2019-06-28 00:00 E01018710 Cheshire West and Chester 020B 6834 3 5497 2 5990 2 17065 6 6629 3 2829 1
{8F1B26BD-62F6-53DB-E053-6C04A8C03649} SK11 8PT 150000 F N L 2019-06-28 00:00 E01018616 Cheshire East 017B 22695 7 23438 8 16657 6 20797 7 12536 4 31565 10
{8F1B26BD-62F7-53DB-E053-6C04A8C03649} CH4 8BN 181000 T N F 2019-07-01 00:00 E01018332 Cheshire West and Chester 043A 19369 6 20352 7 14454 5 17294 6 12352 4 32096 10
{8F1B26BD-62F8-53DB-E053-6C04A8C03649} CH44 4AG 76000 T N F 2019-07-05 00:00 E01007271 Wirral 010E 2379 1 3059 1 2036 1 6994 3 789 1 6371 2
{8F1B26BD-62F9-53DB-E053-6C04A8C03649} CH64 6QF 675000 D N F 2019-06-28 00:00 E01018537 Cheshire West and Chester 001D 28866 9 29218 9 28400 9 13059 4 24395 8 31922 10
{8F1B26BD-62FA-53DB-E053-6C04A8C03649} SK12 1PJ 370000 S N F 2019-06-17 00:00 E01018664 Cheshire East 002D 28920 9 31201 10 27050 9 27976 9 23347 8 32118 10
{8F1B26BD-62FB-53DB-E053-6C04A8C03649} CH1 3JQ 160000 T N F 2019-06-11 00:00 E01018327 Cheshire West and Chester 033B 7928 3 7275 3 6343 2 11164 4 2687 1 15965 5
{8F1B26BD-62FC-53DB-E053-6C04A8C03649} CW2 5AN 270000 D N F 2019-06-26 00:00 E01018494 Cheshire East 048B 27976 9 29451 9 24171 8 23204 8 24754 8 23428 8
{8F1B26BD-62FD-53DB-E053-6C04A8C03649} WA1 3HF 183000 S N L 2019-07-12 00:00 E01012549 Warrington 012D 27914 9 27017 9 24749 8 22810 7 18431 6 19938 7
{8F1B26BD-64EC-53DB-E053-6C04A8C03649} CW1 6DY 150000 S N F 2019-06-28 00:00 E01018501 Cheshire East 038C 15053 5 13836 5 13074 4 20945 7 11263 4 9360 3
{8F1B26BD-64ED-53DB-E053-6C04A8C03649} WA3 4LU 435000 D N F 2019-06-21 00:00 E01012471 Warrington 001A 23541 8 31573 10 27872 9 17285 6 10383 4 16402 5
{8F1B26BD-64EE-53DB-E053-6C04A8C03649} CW9 5RH 169500 S N F 2019-07-18 00:00 E01018717 Cheshire West and Chester 021D 13737 5 14565 5 14229 5 14030 5 12519 4 9776 3
{8F1B26BD-64EF-53DB-E053-6C04A8C03649} CW11 1BE 280000 D N F 2019-07-05 00:00 E01018438 Cheshire East 030C 32165 10 30968 10 30162 10 31532 10 21386 7 29657 10
{8F1B26BD-64F0-53DB-E053-6C04A8C03649} WA5 3LB 265000 D N F 2019-06-07 00:00 E01012494 Warrington 015D 28971 9 28076 9 28009 9 29793 10 20667 7 27114 9
{8F1B26BD-64F1-53DB-E053-6C04A8C03649} CH2 3FA 249950 D N F 2019-07-19 00:00 E01018344 Cheshire West and Chester 036B 27769 9 25180 8 26945 9 20509 7 23219 8 27073 9
{8F1B26BD-64F2-53DB-E053-6C04A8C03649} CW1 4AY 126000 S N F 2019-06-21 00:00 E01018464 Cheshire East 039B 8079 3 12672 4 16609 6 2657 1 8268 3 5123 2
{8F1B26BD-64F3-53DB-E053-6C04A8C03649} WA8 4YR 235000 D N F 2019-06-12 00:00 E01012421 Halton 002D 23303 8 23620 8 20115 7 19783 7 11216 4 24055 8
{8F1B26BD-64F4-53DB-E053-6C04A8C03649} CW11 3GZ 187500 D N F 2019-06-21 00:00 E01018441 Cheshire East 031B 23882 8 27976 9 25408 8 23072 8 14545 5 28557 9
{8F1B26BD-64F5-53DB-E053-6C04A8C03649} SK12 1HY 789000 D N F 2019-06-14 00:00 E01018663 Cheshire East 002C 31766 10 30554 10 29948 10 21881 7 25160 8 30941 10
{8F1B26BD-64F6-53DB-E053-6C04A8C03649} WA5 3RE 147950 T N F 2019-06-20 00:00 E01012494 Warrington 015D 28971 9 28076 9 28009 9 29793 10 20667 7 27114 9
{8F1B26BD-64F7-53DB-E053-6C04A8C03649} CH66 1LP 104000 T N F 2019-06-21 00:00 E01018546 Cheshire West and Chester 005A 4444 2 4655 2 2710 1 13895 5 2296 1 4423 2
{8F1B26BD-64F8-53DB-E053-6C04A8C03649} CW8 1HP 90000 T N F 2019-06-21 00:00 E01018709 Cheshire West and Chester 020A 12929 4 14788 5 12521 4 15842 5 7753 3 14132 5
{8F1B26BD-64F9-53DB-E053-6C04A8C03649} CH4 9EY 295000 D N F 2019-07-01 00:00 E01018335 Cheshire West and Chester 046A 20543 7 25696 8 26346 9 26278 9 26404 9 25716 8
{8F1B26BD-64FA-53DB-E053-6C04A8C03649} WA16 9QJ 180000 S N F 2019-07-01 00:00 E01018722 Cheshire West and Chester 017E 17312 6 25352 8 21860 7 21910 7 18157 6 23555 8
{8F1B26BD-64FC-53DB-E053-6C04A8C03649} CW12 4QP 199950 S N F 2019-06-28 00:00 E01018408 Cheshire East 028B 30548 10 28430 9 24339 8 30050 10 25750 8 26100 8
{8F1B26BD-64FD-53DB-E053-6C04A8C03649} SK11 7EF 480000 D N F 2019-03-27 00:00 E01018628 Cheshire East 016B 29720 10 27433 9 25199 8 23746 8 24355 8 28569 9
{8F1B26BD-64FE-53DB-E053-6C04A8C03649} CW9 7GB 168000 S N F 2019-06-07 00:00 E01018719 Cheshire West and Chester 021E 14244 5 12532 4 11428 4 26572 9 7119 3 11349 4
{8F1B26BD-64FF-53DB-E053-6C04A8C03649} WA5 3SB 310000 D N F 2019-05-23 00:00 E01012531 Warrington 019E 26499 9 24501 8 20606 7 32280 10 19204 6 20416 7
{8F1B26BD-6500-53DB-E053-6C04A8C03649} CW2 6PR 360000 D N F 2019-07-11 00:00 E01018507 Cheshire East 046F 31944 10 31112 10 25111 8 28322 9 25376 8 31037 10
{8F1B26BD-6501-53DB-E053-6C04A8C03649} CW6 9RN 347000 T N F 2019-07-05 00:00 E01018481 Cheshire East 051D 16522 6 27382 9 24995 8 23050 8 30343 10 27065 9
{8F1B26BD-6502-53DB-E053-6C04A8C03649} WA16 8LN 420000 S N F 2019-06-14 00:00 E01018608 Cheshire East 010E 32707 10 32502 10 32194 10 31039 10 29692 10 32692 10
{8F1B26BD-6504-53DB-E053-6C04A8C03649} CW7 3BF 150000 D N F 2019-06-21 00:00 E01018749 Cheshire West and Chester 035D 18635 6 14219 5 17066 6 23250 8 18377 6 10474 4
{8F1B26BD-6505-53DB-E053-6C04A8C03649} CW5 7JB 265000 D N F 2019-06-19 00:00 E01018508 Cheshire East 048D 28331 9 31988 10 27658 9 28936 9 19274 6 27519 9
{8F1B26BD-6506-53DB-E053-6C04A8C03649} WA2 7PP 110000 T N F 2019-04-26 00:00 E01012525 Warrington 011F 5292 2 7261 3 6308 2 6134 2 3955 2 6549 2
{8F1B26BD-6507-53DB-E053-6C04A8C03649} ST7 3EX 225000 D N F 2019-06-28 00:00 E01018417 Cheshire East 033B 22347 7 22321 7 19988 7 18402 6 18852 6 24239 8
{8F1B26BD-6508-53DB-E053-6C04A8C03649} CW2 6GW 235000 D N F 2019-07-12 00:00 E01018506 Cheshire East 046E 32586 10 31522 10 30386 10 30638 10 26681 9 30290 10
{8F1B26BD-6509-53DB-E053-6C04A8C03649} CW11 1EP 152500 F N L 2019-07-15 00:00 E01018442 Cheshire East 031C 27277 9 26172 8 26492 9 16587 6 25774 8 29741 10
{8F1B26BD-650A-53DB-E053-6C04A8C03649} WA5 3NF 221000 S N L 2019-06-28 00:00 E01012493 Warrington 015C 28088 9 25301 8 23539 8 30243 10 16771 6 28151 9
{8F1B26BD-650B-53DB-E053-6C04A8C03649} CW9 8FS 222500 D N F 2019-07-05 00:00 E01018700 Cheshire West and Chester 023D 32794 10 31863 10 31936 10 29900 10 29438 9 31731 10
{8F1B26BD-650C-53DB-E053-6C04A8C03649} CW1 4AA 92000 T N F 2019-05-10 00:00 E01018464 Cheshire East 039B 8079 3 12672 4 16609 6 2657 1 8268 3 5123 2
{8F1B26BD-4595-53DB-E053-6C04A8C03649} N1 9BT 340000 F N L 2019-07-15 00:00 E01002711 Islington 021B 12915 4 12596 4 14988 5 19340 6 12997 4 28538 9
{8F1B26BD-4596-53DB-E053-6C04A8C03649} HA8 9UY 305000 F N L 2019-06-07 00:00 E01000192 Barnet 018A 18475 6 15682 5 19723 7 8607 3 29094 9 25275 8
{8355F009-BD69-55C5-E053-6B04A8C0D090} BH2 5PS 130000 O N L 2019-01-14 00:00 E01033285 Bournemouth 017G 11627 4 12492 4 20069 7 2624 1 10201 4 12209 4
{8355F009-BD6C-55C5-E053-6B04A8C0D090} SP8 4HB 396000 O N F 2019-01-31 00:00 E01020450 North Dorset 002B 29201 9 26461 9 29108 9 30263 10 31744 10 18439 6
{8355F009-BD6D-55C5-E053-6B04A8C0D090} SP8 4EQ 197500 D N F 2019-01-11 00:00 E01020447 North Dorset 001B 17341 6 14400 5 16603 6 28663 9 16652 6 6405 2
{8355F009-BD6E-55C5-E053-6B04A8C0D090} BH4 9JY 230000 F N L 2019-01-28 00:00 E01033286 Bournemouth 023C 20471 7 18636 6 23196 8 9966 4 19477 6 23657 8
{8355F009-BD6F-55C5-E053-6B04A8C0D090} BH12 1AW 215500 F N L 2019-01-17 00:00 E01015386 Poole 009A 9710 3 8440 3 11402 4 9071 3 14073 5 9960 4
{8355F009-BD70-55C5-E053-6B04A8C0D090} DT11 7FS 242000 O N F 2019-02-08 00:00 E01020436 North Dorset 007B 21670 7 19476 6 22455 7 28330 9 29399 9 13968 5
{8355F009-BD71-55C5-E053-6B04A8C0D090} BH16 6EH 900000 D N F 2019-01-22 00:00 E01020470 Purbeck 001C 32050 10 28118 9 30187 10 27918 9 27412 9 29132 9
{8355F009-BD72-55C5-E053-6B04A8C0D090} SP8 5PX 427000 O N F 2019-02-01 00:00 E01020450 North Dorset 002B 29201 9 26461 9 29108 9 30263 10 31744 10 18439 6
{8355F009-BD75-55C5-E053-6B04A8C0D090} BH2 6BY 262000 F N L 2019-01-16 00:00 E01033286 Bournemouth 023C 20471 7 18636 6 23196 8 9966 4 19477 6 23657 8
{8355F009-BD76-55C5-E053-6B04A8C0D090} BH1 1LD 150000 O N L 2019-02-11 00:00 E01015285 Bournemouth 021A 12504 4 16352 5 16440 6 2575 1 12327 4 24052 8
{8355F009-BD77-55C5-E053-6B04A8C0D090} DT7 3DB 149500 F N L 2019-02-04 00:00 E01020532 West Dorset 005B 28011 9 20444 7 28360 9 24050 8 27884 9 27934 9
{8355F009-BD7B-55C5-E053-6B04A8C0D090} SP7 0QW 350000 O N F 2019-02-15 00:00 E01020462 North Dorset 002D 15688 5 22914 7 24266 8 29905 10 26957 9 20077 7
{8355F009-BD7D-55C5-E053-6B04A8C0D090} DT6 4BP 668375 D N F 2019-01-25 00:00 E01020504 West Dorset 008C 24687 8 25394 8 22571 7 29370 9 25545 8 21969 7
{8355F009-BD99-55C5-E053-6B04A8C0D090} BH10 5JG 285000 D N F 2019-01-31 00:00 E01015305 Bournemouth 001C 6513 2 5926 2 6651 3 11921 4 6775 3 4125 2
{8355F009-BD9A-55C5-E053-6B04A8C0D090} DT9 6HY 455000 O N F 2019-01-25 00:00 E01020508 West Dorset 002B 18750 6 27025 9 29294 9 32489 10 29843 10 22885 7
{8355F009-BD9B-55C5-E053-6B04A8C0D090} DT5 2NQ 80000 O N F 2019-02-06 00:00 E01020566 Weymouth and Portland 009D 18877 6 21250 7 17951 6 28058 9 14590 5 13172 5
{8355F009-BD9C-55C5-E053-6B04A8C0D090} DT2 8GA 200000 O Y F 2019-01-31 00:00 E01020537 West Dorset 011B 20194 7 24056 8 22800 7 22341 7 26658 9 19306 6
{8355F009-BD9E-55C5-E053-6B04A8C0D090} BH10 4EB 265000 D N F 2019-01-31 00:00 E01015337 Bournemouth 003E 19516 6 17025 6 17353 6 22193 7 12373 4 17550 6
{8355F009-BDA0-55C5-E053-6B04A8C0D090} BH22 9DP 395000 O N F 2019-02-08 00:00 E01020399 East Dorset 011B 27760 9 24594 8 23127 8 26396 9 29491 9 20274 7
{8355F009-BDA1-55C5-E053-6B04A8C0D090} BH1 4EX 78000 F N L 2019-02-21 00:00 E01015282 Bournemouth 019E 528 1 1747 1 511 1 902 1 610 1 4798 2
{8355F009-BDA3-55C5-E053-6B04A8C0D090} BH14 9AH 449000 D N F 2019-02-08 00:00 E01015462 Poole 013D 19863 7 18021 6 16270 5 17852 6 18757 6 26927 9
{8355F009-BDA5-55C5-E053-6B04A8C0D090} BH8 0AG 340000 O N F 2019-02-08 00:00 E01015352 Bournemouth 004D 21029 7 17923 6 20880 7 29057 9 20406 7 14595 5
{8355F009-BDA6-55C5-E053-6B04A8C0D090} BH17 9DU 245000 T N F 2019-02-15 00:00 E01015415 Poole 004D 6851 3 5823 2 6459 2 20408 7 4879 2 3047 1
{8355F009-BDA7-55C5-E053-6B04A8C0D090} BH9 1HR 260000 O N F 2019-02-11 00:00 E01015373 Bournemouth 010D 18427 6 18130 6 16934 6 16166 5 13595 5 22284 7
{8355F009-BDA8-55C5-E053-6B04A8C0D090} DT4 8DN 350000 O N F 2019-02-15 00:00 E01020554 Weymouth and Portland 004B 1142 1 4292 2 1687 1 303 1 398 1 4008 2
{8355F009-BDA9-55C5-E053-6B04A8C0D090} BH15 4JY 115000 O N F 2019-02-14 00:00 E01015427 Poole 014A 15147 5 17227 6 15524 5 23087 8 12344 4 10644 4
{8355F009-BDAA-55C5-E053-6B04A8C0D090} BH21 6SX 485000 O N F 2019-01-31 00:00 E01020412 East Dorset 003B 13434 5 12062 4 15189 5 14255 5 26259 8 4911 2
{8355F009-BDAD-55C5-E053-6B04A8C0D090} SR7 7RQ 55000 T N F 2019-01-25 00:00 E01020742 County Durham 017C 6644 3 5544 2 3806 2 12676 4 3824 2 8144 3
{8355F009-BDAE-55C5-E053-6B04A8C0D090} DH1 1ST 2280000 O N F 2019-02-15 00:00 E01020690 County Durham 028C 21974 7 20486 7 17865 6 21414 7 9784 3 23670 8
{8355F009-BDAF-55C5-E053-6B04A8C0D090} SR8 4LX 23000 T N F 2019-01-17 00:00 E01020764 County Durham 036A 291 1 748 1 77 1 29 1 527 1 915 1
{8355F009-BDB0-55C5-E053-6B04A8C0D090} SR8 4BH 44000 T N F 2019-02-08 00:00 E01020764 County Durham 036A 291 1 748 1 77 1 29 1 527 1 915 1
{8355F009-BDB2-55C5-E053-6B04A8C0D090} SR8 4BZ 28000 T N F 2019-01-31 00:00 E01020767 County Durham 036C 569 1 1552 1 207 1 56 1 155 1 3016 1
{8355F009-B361-55C5-E053-6B04A8C0D090} WF9 2FR 125000 D N F 2019-01-25 00:00 E01011862 Wakefield 044C 4599 2 6018 2 4289 2 3095 1 2352 1 4848 2
{8355F009-B362-55C5-E053-6B04A8C0D090} WF2 0DU 130000 T N F 2019-02-08 00:00 E01011924 Wakefield 019C 12988 4 15330 5 16328 5 6176 2 11892 4 9673 3
{8355F009-B364-55C5-E053-6B04A8C0D090} LS8 3AP 159000 S N F 2019-02-08 00:00 E01011424 Leeds 047B 4665 2 6380 2 7812 3 672 1 10778 4 3190 1
{8355F009-B366-55C5-E053-6B04A8C0D090} LS19 6QA 240000 T N F 2019-01-18 00:00 E01011457 Leeds 027C 28797 9 30576 10 31781 10 25620 8 20632 7 31836 10
{8355F009-B367-55C5-E053-6B04A8C0D090} BD9 4LS 65000 S N F 2019-02-08 00:00 E01010793 Bradford 025D 6952 3 9378 3 10305 4 2821 1 10677 4 10693 4
{8355F009-B369-55C5-E053-6B04A8C0D090} HD4 5SS 109000 T N L 2019-01-31 00:00 E01011154 Kirklees 045D 9774 3 7205 3 12093 4 8190 3 11745 4 5816 2
{8355F009-B36A-55C5-E053-6B04A8C0D090} BD12 0JT 142000 S N F 2019-01-17 00:00 E01010865 Bradford 060D 11215 4 17515 6 15988 5 3657 2 11571 4 8499 3
{8355F009-B36D-55C5-E053-6B04A8C0D090} BD18 2BW 138000 S N F 2019-02-08 00:00 E01010779 Bradford 021C 4351 2 5552 2 4775 2 617 1 4912 2 4421 2
{8355F009-B36E-55C5-E053-6B04A8C0D090} BD22 8JL 161250 T N F 2019-02-01 00:00 E01010854 Bradford 023A 12588 4 16525 6 12904 4 8196 3 10244 4 15048 5
{8355F009-B370-55C5-E053-6B04A8C0D090} WF4 5DE 104000 T N F 2019-02-08 00:00 E01011802 Wakefield 031D 17370 6 15920 5 16245 5 11040 4 14747 5 17545 6
{8355F009-B371-55C5-E053-6B04A8C0D090} BD10 0AT 109000 T N F 2019-02-04 00:00 E01010654 Bradford 027C 1052 1 1737 1 1940 1 19 1 3332 2 598 1
{8355F009-B375-55C5-E053-6B04A8C0D090} WF1 2FJ 270000 D N F 2019-02-15 00:00 E01011887 Wakefield 008B 26407 9 29115 9 27929 9 15250 5 19308 6 21217 7
{8355F009-B377-55C5-E053-6B04A8C0D090} WF2 8JL 193000 S N F 2019-01-07 00:00 E01011903 Wakefield 028B 8408 3 13086 4 5029 2 4211 2 4734 2 11297 4
{8355F009-B37B-55C5-E053-6B04A8C0D090} HD7 5HN 535000 D N F 2019-01-31 00:00 E01011073 Kirklees 049C 10245 4 10698 4 10042 4 13037 4 9074 3 12997 4
{8355F009-B37D-55C5-E053-6B04A8C0D090} LS17 5BE 140000 F N L 2019-02-07 00:00 E01011514 Leeds 025E 23267 8 26571 9 22732 7 12059 4 19655 6 28897 9
{8355F009-B37F-55C5-E053-6B04A8C0D090} HX7 8DZ 220000 F N L 2019-01-25 00:00 E01010888 Calderdale 004E 14403 5 14517 5 14232 5 15357 5 11848 4 28137 9
{8355F009-B380-55C5-E053-6B04A8C0D090} WF8 2QS 152500 S N F 2019-01-31 00:00 E01011853 Wakefield 018D 6102 2 6026 2 4905 2 11764 4 5779 2 2608 1
{8355F009-B382-55C5-E053-6B04A8C0D090} LS4 2SE 45000 F N L 2019-01-31 00:00 E01011480 Leeds 045B 16197 5 24348 8 20838 7 2286 1 14533 5 20462 7
{8355F009-B386-55C5-E053-6B04A8C0D090} WF14 9FA 208000 S N F 2019-01-14 00:00 E01011216 Kirklees 021A 24655 8 24086 8 25731 8 9270 3 16691 6 27945 9
{8355F009-B387-55C5-E053-6B04A8C0D090} WF5 9EH 187500 S N F 2019-01-11 00:00 E01011828 Wakefield 021F 19711 7 20260 7 23464 8 10659 4 17072 6 13180 5
{8355F009-B388-55C5-E053-6B04A8C0D090} LS19 7HG 162000 T N F 2019-02-01 00:00 E01011273 Leeds 010B 19375 6 19671 6 19396 6 13259 5 12938 4 22866 7
{8355F009-B389-55C5-E053-6B04A8C0D090} HD3 3FW 123000 T N F 2019-01-25 00:00 E01011070 Kirklees 052A 18464 6 20281 7 21895 7 18121 6 25480 8 23408 8
{8355F009-B38D-55C5-E053-6B04A8C0D090} WF6 1EP 98500 T N F 2019-02-18 00:00 E01011822 Wakefield 016F 8959 3 12970 4 12406 4 1176 1 6558 2 6094 2
{8355F009-B38E-55C5-E053-6B04A8C0D090} LS13 1GH 195000 T N F 2019-01-18 00:00 E01011325 Leeds 046B 7939 3 10261 4 8559 3 2372 1 7083 3 8708 3
{8355F009-B390-55C5-E053-6B04A8C0D090} BD6 2AR 137000 S N F 2019-01-14 00:00 E01010867 Bradford 059G 8448 3 6875 3 9708 3 3745 2 8953 3 5796 2
{8355F009-B391-55C5-E053-6B04A8C0D090} HD9 2AA 510000 D N F 2019-01-10 00:00 E01011180 Kirklees 058A 27915 9 27554 9 27065 9 25146 8 26524 9 28859 9
{8355F009-B395-55C5-E053-6B04A8C0D090} BD22 7FA 168000 D N F 2019-02-15 00:00 E01010862 Bradford 014C 23732 8 21529 7 22751 7 22542 7 14862 5 20650 7
{8355F009-B396-55C5-E053-6B04A8C0D090} WF1 3PR 64000 F N L 2019-02-01 00:00 E01011912 Wakefield 017B 3384 2 5426 2 2354 1 2308 1 5052 2 3093 1
{8355F009-B71B-55C5-E053-6B04A8C0D090} BS6 7SS 450000 T N F 2019-02-06 00:00 E01014620 Bristol 011D 30412 10 30612 10 25055 8 14399 5 28682 9 32271 10
{8355F009-B71C-55C5-E053-6B04A8C0D090} BS3 1HT 210000 T N F 2019-01-29 00:00 E01033361 Bristol 039F 4598 2 6998 3 8546 3 67 1 1348 1 17469 6
{8355F009-B71D-55C5-E053-6B04A8C0D090} BS16 1QB 225000 F N L 2019-01-25 00:00 E01014660 Bristol 014A 4499 2 5337 2 5603 2 4469 2 4797 2 2355 1
{8355F009-B722-55C5-E053-6B04A8C0D090} BS3 2BX 450000 O N F 2019-01-31 00:00 E01014700 Bristol 036E 25217 8 30726 10 27006 9 9798 3 20989 7 20290 7
{8355F009-B724-55C5-E053-6B04A8C0D090} BS3 5DP 175000 F N L 2019-01-15 00:00 E01014579 Bristol 045A 6107 2 6939 3 7118 3 5296 2 6873 3 4344 2
{8355F009-B725-55C5-E053-6B04A8C0D090} BS3 5DP 195000 F N L 2019-01-14 00:00 E01014579 Bristol 045A 6107 2 6939 3 7118 3 5296 2 6873 3 4344 2
{8355F009-B730-55C5-E053-6B04A8C0D090} BS1 6FL 488334 O N L 2019-02-05 00:00 E01033355 Bristol 054C 8878 3 11556 4 11748 4 12190 4 9347 3 9787 3
{8355F009-B731-55C5-E053-6B04A8C0D090} BS7 0JP 280000 T N L 2019-02-08 00:00 E01014634 Bristol 005B 23040 8 25858 8 30224 10 6434 2 17408 6 24755 8
{8355F009-B735-55C5-E053-6B04A8C0D090} BS5 7SP 229000 T N F 2019-01-23 00:00 E01014626 Bristol 021C 6515 2 6726 3 7207 3 5626 2 6937 3 8111 3
{8355F009-B737-55C5-E053-6B04A8C0D090} BS6 6SU 825000 T N F 2019-01-11 00:00 E01014559 Bristol 022C 26995 9 27939 9 31104 10 18496 6 20839 7 31117 10
{8355F009-B73A-55C5-E053-6B04A8C0D090} BS8 4RJ 975000 O N F 2019-02-06 00:00 E01014549 Bristol 034B 15519 5 15656 5 15643 5 15152 5 18901 6 28271 9
{8355F009-B73B-55C5-E053-6B04A8C0D090} BS1 5JG 950000 O N F 2019-02-21 00:00 E01014542 Bristol 032C 18266 6 29666 10 32266 10 6443 2 10281 4 31234 10
{8355F009-B73C-55C5-E053-6B04A8C0D090} BS7 8JJ 820 O N F 2019-02-11 00:00 E01014666 Bristol 017C 30477 10 30416 10 30609 10 15338 5 30143 10 32204 10
{8355F009-B73D-55C5-E053-6B04A8C0D090} BS7 8JJ 820 O N F 2019-01-11 00:00 E01014666 Bristol 017C 30477 10 30416 10 30609 10 15338 5 30143 10 32204 10
{8355F009-B73E-55C5-E053-6B04A8C0D090} BS7 8JJ 820 O N F 2019-02-11 00:00 E01014666 Bristol 017C 30477 10 30416 10 30609 10 15338 5 30143 10 32204 10
{8355F009-B73F-55C5-E053-6B04A8C0D090} BS3 5AU 290000 O N F 2019-01-09 00:00 E01014736 Bristol 040D 9837 3 11490 4 12021 4 7610 3 7188 3 6364 2
{8355F009-B741-55C5-E053-6B04A8C0D090} BS2 8HD 410000 O N F 2019-01-21 00:00 E01014542 Bristol 032C 18266 6 29666 10 32266 10 6443 2 10281 4 31234 10
{8355F009-B742-55C5-E053-6B04A8C0D090} BS6 6JA 465000 O N F 2019-01-30 00:00 E01014560 Bristol 022D 25109 8 28102 9 29421 9 13123 4 20345 7 32148 10
{8355F009-B743-55C5-E053-6B04A8C0D090} BS13 8LU 245000 T N F 2019-01-21 00:00 E01014515 Bristol 050A 4064 2 5842 2 5675 2 1744 1 5305 2 1203 1
{8355F009-B744-55C5-E053-6B04A8C0D090} BS14 9EA 380000 S N F 2019-02-04 00:00 E01014708 Bristol 049G 15316 5 18647 6 16812 6 5709 2 15395 5 10884 4
{8355F009-B745-55C5-E053-6B04A8C0D090} BS14 9AL 115000 F N L 2019-02-13 00:00 E01014613 Bristol 047E 15211 5 17190 6 16563 6 11370 4 13886 5 8890 3
{8355F009-B746-55C5-E053-6B04A8C0D090} BS1 1RU 40500 O N F 2019-01-16 00:00 E01014540 Bristol 032B 12154 4 21851 7 26159 8 1511 1 16466 6 12672 4
{8355F009-B747-55C5-E053-6B04A8C0D090} BS14 0AH 220000 S N F 2019-01-28 00:00 E01014607 Bristol 047A 11390 4 15461 5 17841 6 613 1 12192 4 8193 3
{8355F009-B748-55C5-E053-6B04A8C0D090} BS7 9ES 343000 O N L 2019-01-22 00:00 E01014508 Bristol 016B 18629 6 24377 8 22507 7 5325 2 13908 5 30400 10
{8355F009-B749-55C5-E053-6B04A8C0D090} BS4 3LT 260000 T N F 2019-01-31 00:00 E01014531 Bristol 035D 18045 6 20893 7 25550 8 10736 4 19931 7 17333 6
{8355F009-B74A-55C5-E053-6B04A8C0D090} BS3 4EY 650000 O N L 2019-02-15 00:00 E01033361 Bristol 039F 4598 2 6998 3 8546 3 67 1 1348 1 17469 6
{8355F009-B74B-55C5-E053-6B04A8C0D090} BS7 0UL 332500 S N F 2019-01-25 00:00 E01014631 Bristol 004A 16300 5 13649 5 13598 5 17356 6 6589 3 24644 8
{8355F009-B74C-55C5-E053-6B04A8C0D090} BS8 1LP 1007500 S N F 2019-01-21 00:00 E01014551 Bristol 026A 20720 7 29405 9 31404 10 10766 4 15353 5 21289 7
{8355F009-B74D-55C5-E053-6B04A8C0D090} BS1 1NR 341763 O N L 2019-01-25 00:00 E01014540 Bristol 032B 12154 4 21851 7 26159 8 1511 1 16466 6 12672 4
{8355F009-B74E-55C5-E053-6B04A8C0D090} MK2 2HN 322500 D N F 2019-01-23 00:00 E01016710 Milton Keynes 030A 10999 4 10903 4 12267 4 3525 2 13902 5 7549 3
{8355F009-BA17-55C5-E053-6B04A8C0D090} TR16 5TP 101000 T N F 2019-01-16 00:00 E01018916 Cornwall 050A 9892 4 10608 4 10340 4 23781 8 11711 4 10435 4
{8355F009-BA1B-55C5-E053-6B04A8C0D090} TR27 5AF 127500 O N F 2019-01-31 00:00 E01018982 Cornwall 057C 11436 4 10212 4 12156 4 19848 7 19434 6 8126 3
{8355F009-BA1C-55C5-E053-6B04A8C0D090} TR18 2HZ 140000 O N F 2019-01-28 00:00 E01018999 Cornwall 068C 3874 2 3910 2 3100 1 3950 2 4366 2 14325 5
{8355F009-BA1D-55C5-E053-6B04A8C0D090} TR7 2TB 332500 D N F 2019-01-21 00:00 E01019018 Cornwall 021A 23996 8 22051 7 19403 6 30336 10 19803 7 25336 8
{8355F009-BA20-55C5-E053-6B04A8C0D090} TR4 9LF 208332 O N F 2019-01-22 00:00 E01018819 Cornwall 043A 20905 7 18065 6 15733 5 30311 10 19882 7 15408 5
{8355F009-BA21-55C5-E053-6B04A8C0D090} TR27 4DY 120000 F N L 2019-02-05 00:00 E01018982 Cornwall 057C 11436 4 10212 4 12156 4 19848 7 19434 6 8126 3
{8355F009-BA22-55C5-E053-6B04A8C0D090} EX23 0EH 222222 O N F 2019-01-18 00:00 E01018951 Cornwall 002B 10912 4 16402 5 16818 6 30514 10 25948 8 13305 5
{8355F009-BA23-55C5-E053-6B04A8C0D090} TR26 3HW 136000 O N F 2019-01-31 00:00 E01018985 Cornwall 058A 19312 6 19990 7 16782 6 31963 10 27628 9 20868 7
{8355F009-BA24-55C5-E053-6B04A8C0D090} PL25 4AN 35000 O N F 2019-01-24 00:00 E01019047 Cornwall 038D 7549 3 9415 3 3632 2 8490 3 7380 3 14247 5
{8355F009-BA27-55C5-E053-6B04A8C0D090} EX23 0EH 265000 O N F 2019-01-17 00:00 E01018951 Cornwall 002B 10912 4 16402 5 16818 6 30514 10 25948 8 13305 5
{8355F009-BA29-55C5-E053-6B04A8C0D090} PL26 7AW 375000 O N F 2019-02-15 00:00 E01019067 Cornwall 041E 14793 5 19553 6 23145 8 28765 9 20079 7 24123 8
{8355F009-BA2A-55C5-E053-6B04A8C0D090} TR19 6JB 500000 O N F 2019-01-14 00:00 E01018992 Cornwall 069A 14154 5 16459 6 17451 6 25368 8 21735 7 25718 8
{8355F009-BA2B-55C5-E053-6B04A8C0D090} PL31 2HU 315000 O N F 2019-01-18 00:00 E01018933 Cornwall 014B 2579 1 2914 1 2402 1 5118 2 2548 1 4027 2
{8355F009-BA2D-55C5-E053-6B04A8C0D090} PL26 6AZ 1220000 O N F 2019-01-24 00:00 E01019067 Cornwall 041E 14793 5 19553 6 23145 8 28765 9 20079 7 24123 8
{8355F009-BA30-55C5-E053-6B04A8C0D090} TR9 6NE 218000 S N F 2019-02-01 00:00 E01019065 Cornwall 019F 13499 5 14322 5 13553 5 32117 10 13125 4 6446 2
{8355F009-BA31-55C5-E053-6B04A8C0D090} PL14 4NP 152000 O N L 2019-01-28 00:00 E01018758 Cornwall 016A 10481 4 12918 4 9861 4 26475 9 12038 4 8075 3
{8355F009-BA33-55C5-E053-6B04A8C0D090} TR7 1NJ 525000 O N F 2019-01-29 00:00 E01019028 Cornwall 020A 8820 3 7407 3 6990 3 10375 4 9599 3 18868 6
{8355F009-BA34-55C5-E053-6B04A8C0D090} TR7 1NA 295000 T N F 2019-02-01 00:00 E01019028 Cornwall 020A 8820 3 7407 3 6990 3 10375 4 9599 3 18868 6
{8355F009-BA35-55C5-E053-6B04A8C0D090} PL26 8EP 146000 O N F 2019-01-25 00:00 E01019052 Cornwall 024A 8372 3 11419 4 11051 4 27662 9 13885 5 4123 2
{8355F009-BA36-55C5-E053-6B04A8C0D090} PL12 5BJ 126770 F N L 2019-01-25 00:00 E01018785 Cornwall 023D 13965 5 19370 6 19601 6 27346 9 22152 7 19921 7
{8355F009-BA37-55C5-E053-6B04A8C0D090} EX23 0DJ 39995 O N L 2019-02-08 00:00 E01018951 Cornwall 002B 10912 4 16402 5 16818 6 30514 10 25948 8 13305 5
{8355F009-BA39-55C5-E053-6B04A8C0D090} TR8 5RL 975000 D N F 2019-01-18 00:00 E01019031 Cornwall 020D 13161 5 15433 5 14997 5 21007 7 15632 5 18348 6
{8355F009-BA3A-55C5-E053-6B04A8C0D090} TR2 4PZ 275000 S N F 2019-02-01 00:00 E01019066 Cornwall 041D 14986 5 16298 5 23697 8 32141 10 20500 7 24220 8
{8355F009-BA3D-55C5-E053-6B04A8C0D090} EX23 8FJ 415000 D N F 2019-01-25 00:00 E01018937 Cornwall 001B 8452 3 5833 2 4231 2 26938 9 15372 5 5817 2
{8355F009-BA3E-55C5-E053-6B04A8C0D090} TR7 2NH 265000 T N F 2019-01-25 00:00 E01019020 Cornwall 021C 3218 1 3593 2 1907 1 7177 3 2829 1 8769 3
{8355F009-BA3F-55C5-E053-6B04A8C0D090} TR27 4QT 168000 D N F 2019-02-07 00:00 E01018983 Cornwall 057D 22261 7 19133 6 18333 6 24886 8 18353 6 23509 8
{8355F009-BA40-55C5-E053-6B04A8C0D090} PL17 7DF 215000 O N F 2019-02-18 00:00 E01018752 Cornwall 013C 17741 6 17682 6 15630 5 26524 9 17091 6 14635 5
{8355F009-BA44-55C5-E053-6B04A8C0D090} PL30 3JZ 550000 O N F 2019-01-31 00:00 E01018940 Cornwall 003A 14805 5 15531 5 15664 5 30958 10 16900 6 20303 7
{8355F009-BA4A-55C5-E053-6B04A8C0D090} PL11 2AW 12000 O N L 2019-01-11 00:00 E01018797 Cornwall 029A 5989 2 5298 2 4216 2 15493 5 2413 1 8906 3
{8355F009-BDB4-55C5-E053-6B04A8C0D090} DL16 6HP 30000 T N F 2019-02-12 00:00 E01020842 County Durham 049D 6159 2 4423 2 3676 2 16229 5 6834 3 4150 2
{8355F009-BDB5-55C5-E053-6B04A8C0D090} DL15 0PD 15000 O N F 2019-02-11 00:00 E01020904 County Durham 046F 11291 4 11700 4 7251 3 3544 2 5781 2 20081 7
{8355F009-BDB6-55C5-E053-6B04A8C0D090} SR8 3DQ 51000 T N F 2019-02-04 00:00 E01020751 County Durham 025A 6088 2 8518 3 2889 1 4439 2 3390 2 6824 3
{8355F009-BDB7-55C5-E053-6B04A8C0D090} DL15 8PY 39999 T N F 2019-01-25 00:00 E01020898 County Durham 045F 906 1 600 1 271 1 12657 4 632 1 3099 1
{8355F009-BDB8-55C5-E053-6B04A8C0D090} DH9 7AW 30000 O N F 2019-01-18 00:00 E01020671 County Durham 009D 1680 1 1675 1 711 1 1905 1 2524 1 3087 1
{8355F009-BDB9-55C5-E053-6B04A8C0D090} SR7 8JW 70000 S N F 2019-02-06 00:00 E01020787 County Durham 016E 8076 3 5781 2 3965 2 13595 5 7221 3 8797 3
{8355F009-BDBA-55C5-E053-6B04A8C0D090} DL14 7DG 32000 T N F 2019-01-18 00:00 E01020872 County Durham 056C 11087 4 10352 4 8918 3 5510 2 5141 2 16544 6
{8355F009-BDBB-55C5-E053-6B04A8C0D090} DL2 2TU 575000 O N F 2019-02-01 00:00 E01012334 Darlington 001B 18804 6 26934 9 25451 8 15696 5 19524 6 28396 9
{8355F009-BDBF-55C5-E053-6B04A8C0D090} DH9 9RT 190000 D N F 2019-01-17 00:00 E01020676 County Durham 001E 16102 5 14831 5 10815 4 28088 9 7027 3 16397 5
{8355F009-BDC0-55C5-E053-6B04A8C0D090} SR8 3LZ 32750 T N F 2019-02-04 00:00 E01020751 County Durham 025A 6088 2 8518 3 2889 1 4439 2 3390 2 6824 3
{8355F009-BDC1-55C5-E053-6B04A8C0D090} DL1 4DT 72000 T N F 2019-02-01 00:00 E01012304 Darlington 012A 303 1 357 1 374 1 766 1 340 1 1088 1
{8355F009-BDC2-55C5-E053-6B04A8C0D090} DH6 1NG 28000 T N F 2019-02-06 00:00 E01020729 County Durham 027D 7365 3 6889 3 5856 2 7074 3 8509 3 8149 3
{8355F009-BDC3-55C5-E053-6B04A8C0D090} DH9 9PJ 45500 T N F 2019-01-21 00:00 E01020676 County Durham 001E 16102 5 14831 5 10815 4 28088 9 7027 3 16397 5
{8355F009-BDC5-55C5-E053-6B04A8C0D090} DL14 7DL 37000 T N F 2019-01-18 00:00 E01020869 County Durham 055B 5895 2 6458 2 4558 2 2551 1 3876 2 11822 4
{8355F009-BDC6-55C5-E053-6B04A8C0D090} SR7 7UB 150000 O N F 2019-02-01 00:00 E01020740 County Durham 017A 14665 5 14158 5 11034 4 12563 4 7333 3 11461 4
{8355F009-BDC8-55C5-E053-6B04A8C0D090} DL3 0NX 73000 T N F 2019-01-11 00:00 E01012349 Darlington 004A 552 1 662 1 643 1 552 1 498 1 1383 1
{8355F009-BDC9-55C5-E053-6B04A8C0D090} DL5 7QD 175000 D N F 2019-02-15 00:00 E01020814 County Durham 060B 15674 5 14750 5 14178 5 11275 4 6367 2 20356 7
{8355F009-BDCA-55C5-E053-6B04A8C0D090} DL3 0GZ 105000 T N F 2019-01-25 00:00 E01012309 Darlington 006A 24336 8 26043 8 21114 7 5952 2 19410 6 23084 8
{8355F009-BDCB-55C5-E053-6B04A8C0D090} SR4 6LZ 45000 T N F 2019-02-19 00:00 E01008757 Sunderland 015A 5051 2 4454 2 3829 2 2127 1 2676 1 11172 4
{8355F009-BDCF-55C5-E053-6B04A8C0D090} DH3 3NE 115000 T N F 2019-01-25 00:00 E01020595 County Durham 013B 24802 8 25526 8 18190 6 13955 5 12879 4 29998 10
{8355F009-BDD0-55C5-E053-6B04A8C0D090} DL14 9SS 170000 O N F 2019-02-06 00:00 E01020907 County Durham 058C 3254 1 2835 1 2167 1 3061 1 4436 2 2558 1
{8355F009-BDD1-55C5-E053-6B04A8C0D090} SR8 4EH 32500 T N F 2019-01-28 00:00 E01020767 County Durham 036C 569 1 1552 1 207 1 56 1 155 1 3016 1
{8355F009-BDD2-55C5-E053-6B04A8C0D090} DL13 4BD 55000 T N F 2019-02-14 00:00 E01020892 County Durham 045D 6236 2 5362 2 4016 2 6095 2 5629 2 8612 3
{8355F009-BDD3-55C5-E053-6B04A8C0D090} DL17 8BP 90000 D N F 2019-01-18 00:00 E01033203 County Durham 053E 5925 2 5137 2 3604 2 4378 2 7174 3 6463 2
{8355F009-BDD4-55C5-E053-6B04A8C0D090} SR8 3PX 17000 T N F 2019-01-17 00:00 E01020752 County Durham 025B 221 1 376 1 33 1 2 1 536 1 1330 1
{8355F009-BDD5-55C5-E053-6B04A8C0D090} DL15 0EW 50000 T N F 2019-01-31 00:00 E01020903 County Durham 046E 11996 4 11856 4 9801 3 8459 3 6820 3 20707 7
{8355F009-BDD7-55C5-E053-6B04A8C0D090} DL14 8UZ 20000 T N F 2019-02-14 00:00 E01020838 County Durham 059C 4453 2 4730 2 3656 2 1052 1 3864 2 7645 3
{8355F009-BDD8-55C5-E053-6B04A8C0D090} DH8 5FE 98500 T N L 2019-02-05 00:00 E01020644 County Durham 014D 10998 4 8960 3 8708 3 12584 4 6863 3 9783 3
{8355F009-BDD9-55C5-E053-6B04A8C0D090} DH7 0TP 600000 O N F 2019-02-01 00:00 E01020664 County Durham 020B 28226 9 27355 9 30613 10 20919 7 19557 6 30070 10
{8355F009-BDDA-55C5-E053-6B04A8C0D090} DH8 5FD 98500 T N L 2019-02-05 00:00 E01020644 County Durham 014D 10998 4 8960 3 8708 3 12584 4 6863 3 9783 3
{8355F009-BDDB-55C5-E053-6B04A8C0D090} DH9 6BA 43000 T N F 2019-02-01 00:00 E01020649 County Durham 008C 7293 3 5876 2 4128 2 11448 4 7242 3 6147 2
{8355F009-BDDD-55C5-E053-6B04A8C0D090} DL1 5AQ 15000 O N L 2019-01-23 00:00 E01012357 Darlington 014B 5090 2 5646 2 6549 2 2172 1 4689 2 3865 2
{8355F009-BDDE-55C5-E053-6B04A8C0D090} DL1 4RY 100000 T N F 2019-01-31 00:00 E01012318 Darlington 013B 2843 1 2761 1 2616 1 1670 1 3014 1 2063 1
{8355F009-B397-55C5-E053-6B04A8C0D090} LS6 3QF 439750 S N F 2019-02-18 00:00 E01011694 Leeds 109F 25853 8 26673 9 31020 10 11703 4 24783 8 29634 10
{8355F009-B39A-55C5-E053-6B04A8C0D090} HX2 0SR 208500 S N F 2019-01-15 00:00 E01010927 Calderdale 009A 16996 6 27065 9 24322 8 639 1 19461 6 20421 7
{8355F009-B39B-55C5-E053-6B04A8C0D090} HX1 2YJ 198500 T N F 2019-01-09 00:00 E01010969 Calderdale 016A 18647 6 19630 6 16351 5 15424 5 15152 5 24787 8
{8355F009-B39D-55C5-E053-6B04A8C0D090} LS28 6FA 228000 S N F 2019-02-04 00:00 E01011596 Leeds 062D 25901 8 29495 9 26309 9 13001 4 21919 7 25788 8
{8355F009-B39F-55C5-E053-6B04A8C0D090} LS29 7EY 295000 T N F 2019-02-15 00:00 E01010769 Bradford 003C 26788 9 25317 8 25648 8 21930 7 20276 7 28466 9
{8355F009-B3A0-55C5-E053-6B04A8C0D090} LS12 6BU 116500 F N L 2019-01-02 00:00 E01011734 Leeds 083A 18442 6 22720 7 24200 8 9328 3 16034 5 19159 6
{8355F009-B3A1-55C5-E053-6B04A8C0D090} LS15 0NJ 199999 D N F 2019-01-29 00:00 E01011616 Leeds 072A 1152 1 2008 1 967 1 1243 1 1393 1 1468 1
{8355F009-B3A2-55C5-E053-6B04A8C0D090} WF11 8QF 65000 S N F 2019-01-22 00:00 E01011804 Wakefield 011B 9611 3 13401 5 9617 3 6109 2 10573 4 3589 2
{8355F009-B3A3-55C5-E053-6B04A8C0D090} LS19 7QA 88000 T N F 2019-01-29 00:00 E01011276 Leeds 011C 19771 7 22476 7 22088 7 7815 3 16183 5 27979 9
{8355F009-B3A4-55C5-E053-6B04A8C0D090} BD12 0EX 130000 T N F 2019-01-29 00:00 E01010741 Bradford 059A 9746 3 15713 5 13633 5 331 1 14652 5 13410 5
{8355F009-B3A6-55C5-E053-6B04A8C0D090} LS25 1NJ 105000 F N L 2019-02-21 00:00 E01011403 Leeds 074D 22784 7 20898 7 21351 7 22708 7 7911 3 27069 9
{8355F009-B3A9-55C5-E053-6B04A8C0D090} LS10 4GA 208000 T N F 2019-02-01 00:00 E01011490 Leeds 099A 18717 6 18474 6 22994 8 4424 2 14765 5 20853 7
{8355F009-B3AB-55C5-E053-6B04A8C0D090} LS18 5JR 330000 S N F 2019-02-01 00:00 E01011455 Leeds 021A 30741 10 32494 10 32706 10 23614 8 26076 8 31363 10
{8355F009-B3AC-55C5-E053-6B04A8C0D090} BD16 2PR 210000 T N F 2019-01-25 00:00 E01010584 Bradford 015D 7752 3 9289 3 9481 3 3413 2 5868 2 8072 3
{8355F009-B3AD-55C5-E053-6B04A8C0D090} WF10 5GU 200000 D N L 2019-01-18 00:00 E01011775 Wakefield 024A 21047 7 20122 7 16080 5 21214 7 20231 7 20059 7
{8355F009-B3B1-55C5-E053-6B04A8C0D090} LS10 4SW 35000 F N L 2019-01-18 00:00 E01011490 Leeds 099A 18717 6 18474 6 22994 8 4424 2 14765 5 20853 7
{8355F009-B3B2-55C5-E053-6B04A8C0D090} HX1 2YH 167500 T N F 2019-01-31 00:00 E01010969 Calderdale 016A 18647 6 19630 6 16351 5 15424 5 15152 5 24787 8
{8355F009-B3B3-55C5-E053-6B04A8C0D090} LS6 3BY 150000 S N F 2019-02-08 00:00 E01011441 Leeds 044E 18606 6 19652 6 20884 7 14304 5 11452 4 25230 8
{8355F009-B3B5-55C5-E053-6B04A8C0D090} BD22 0QG 203000 T N F 2019-01-18 00:00 E01010722 Bradford 014A 2233 1 3886 2 3623 2 177 1 3809 2 2580 1
{8355F009-B3B7-55C5-E053-6B04A8C0D090} WF7 6FG 124850 T N F 2019-01-28 00:00 E01011778 Wakefield 024D 11184 4 12037 4 8731 3 12353 4 7945 3 5360 2
{8355F009-B3B8-55C5-E053-6B04A8C0D090} BD13 2FQ 208000 S N F 2019-01-04 00:00 E01010752 Bradford 058A 6179 2 10508 4 8629 3 1529 1 6432 2 14222 5
{8355F009-B3B9-55C5-E053-6B04A8C0D090} HD7 5RD 127000 T N L 2019-02-08 00:00 E01011077 Kirklees 045A 12793 4 17371 6 18115 6 10005 4 15742 5 16773 6
{8355F009-B3BB-55C5-E053-6B04A8C0D090} HX5 0DX 175000 T N F 2019-02-08 00:00 E01010894 Calderdale 025D 3768 2 4313 2 5243 2 7830 3 7794 3 3754 2
{8355F009-B3BC-55C5-E053-6B04A8C0D090} LS6 4AE 107000 F N L 2019-01-14 00:00 E01011692 Leeds 109D 22555 7 26422 9 26267 8 6531 2 22772 7 32673 10
{8355F009-B3BD-55C5-E053-6B04A8C0D090} LS10 4FP 95000 F N L 2019-02-08 00:00 E01011490 Leeds 099A 18717 6 18474 6 22994 8 4424 2 14765 5 20853 7
{8355F009-B3BE-55C5-E053-6B04A8C0D090} WF8 3AD 320000 D N F 2019-01-31 00:00 E01011849 Wakefield 034E 21151 7 21988 7 19979 7 11313 4 18198 6 25486 8
{8355F009-B3BF-55C5-E053-6B04A8C0D090} HX2 8AF 130000 S N F 2019-01-31 00:00 E01010939 Calderdale 005B 16387 5 23239 8 18862 6 1208 1 13984 5 20582 7
{8355F009-B3C0-55C5-E053-6B04A8C0D090} BD5 7HT 64000 S N F 2019-01-29 00:00 E01010736 Bradford 048D 2289 1 4456 2 5429 2 1774 1 7504 3 314 1
{8355F009-B3C4-55C5-E053-6B04A8C0D090} CR5 2YS 715000 O N F 2019-01-11 00:00 E01001023 Croydon 041C 11765 4 11262 4 9844 3 13090 4 14367 5 22719 7
{8355F009-B3C5-55C5-E053-6B04A8C0D090} SW1V 4NU 1800000 T N F 2019-02-04 00:00 E01004751 Westminster 022C 24060 8 22710 7 25729 8 10587 4 31316 10 30756 10
{8355F009-B74F-55C5-E053-6B04A8C0D090} MK16 8TH 335000 O N F 2019-02-01 00:00 E01016788 Milton Keynes 003C 27067 9 28885 9 29173 9 23939 8 28286 9 21439 7
{8355F009-B751-55C5-E053-6B04A8C0D090} MK13 8BS 145000 F N L 2019-01-18 00:00 E01016724 Milton Keynes 013D 12503 4 10822 4 13122 4 9358 3 11901 4 11627 4
{8355F009-B752-55C5-E053-6B04A8C0D090} LU6 2RP 475000 S N F 2019-02-06 00:00 E01017658 Aylesbury Vale 009D 26147 8 26237 8 30293 10 28462 9 28123 9 27678 9
{8355F009-B753-55C5-E053-6B04A8C0D090} MK11 1NN 210000 S N F 2019-01-31 00:00 E01016817 Milton Keynes 010B 17886 6 14425 5 15901 5 15536 5 21533 7 20248 7
{8355F009-B754-55C5-E053-6B04A8C0D090} HP10 8AD 250000 O N F 2019-01-18 00:00 E01017784 Chiltern 013E 31154 10 32073 10 32162 10 31069 10 32737 10 30390 10
{8355F009-B755-55C5-E053-6B04A8C0D090} HP14 4NN 820000 O N F 2019-02-15 00:00 E01017872 Wycombe 003D 31474 10 31784 10 32168 10 32788 10 32397 10 29760 10
{8355F009-B758-55C5-E053-6B04A8C0D090} MK2 3NU 172000 T N F 2019-02-05 00:00 E01016744 Milton Keynes 032C 3158 1 2827 1 5532 2 6787 3 7779 3 3468 2
{8355F009-B759-55C5-E053-6B04A8C0D090} MK11 3HE 240000 O N F 2019-02-12 00:00 E01016810 Milton Keynes 012A 3304 2 4139 2 2368 1 10525 4 4831 2 5284 2
{8355F009-B75A-55C5-E053-6B04A8C0D090} SL3 6BH 280000 F N L 2019-02-15 00:00 E01017834 South Bucks 005D 19887 7 25098 8 28772 9 4877 2 29961 10 17100 6
{8355F009-B75C-55C5-E053-6B04A8C0D090} MK13 0EQ 170000 T N F 2019-02-08 00:00 E01016835 Milton Keynes 006D 6027 2 6268 2 7152 3 7212 3 6210 2 4986 2
{8355F009-B75D-55C5-E053-6B04A8C0D090} SL0 9JQ 13021125 O N F 2019-02-08 00:00 E01017827 South Bucks 008D 18326 6 18529 6 20080 7 8841 3 26903 9 12038 4
{8355F009-B75E-55C5-E053-6B04A8C0D090} HP11 2TL 725000 D N F 2019-02-11 00:00 E01017838 Wycombe 015C 24120 8 18290 6 24536 8 24000 8 30357 10 19917 7
{8355F009-B760-55C5-E053-6B04A8C0D090} HP12 3BD 450000 O N F 2019-02-07 00:00 E01017904 Wycombe 013B 12505 4 8580 3 10926 4 17203 6 21030 7 7350 3
{8355F009-B761-55C5-E053-6B04A8C0D090} MK6 2PJ 250000 T N F 2019-02-01 00:00 E01016725 Milton Keynes 021A 9735 3 8015 3 11590 4 10635 4 15350 5 10714 4
{8355F009-B762-55C5-E053-6B04A8C0D090} MK14 7LJ 220000 T N F 2019-01-29 00:00 E01016768 Milton Keynes 014D 15798 5 14323 5 18635 6 7754 3 18484 6 16589 6
{8355F009-B763-55C5-E053-6B04A8C0D090} HP8 4SL 1900000 O N F 2019-01-16 00:00 E01017762 Chiltern 008A 31470 10 32827 10 32739 10 31874 10 32734 10 32533 10
{8355F009-B764-55C5-E053-6B04A8C0D090} HP12 3SL 19625000 O N F 2019-01-18 00:00 E01017840 Wycombe 015E 27778 9 21954 7 21758 7 29557 9 28027 9 24656 8
{8355F009-B765-55C5-E053-6B04A8C0D090} MK14 6HJ 235000 O N F 2019-02-15 00:00 E01016771 Milton Keynes 005E 15264 5 11494 4 13454 5 19167 6 10576 4 15045 5
{8355F009-B766-55C5-E053-6B04A8C0D090} HP27 9DE 472000 O N F 2019-01-11 00:00 E01017931 Wycombe 001C 23821 8 21642 7 15666 5 25371 8 26712 9 15335 5
{8355F009-B767-55C5-E053-6B04A8C0D090} MK4 2EH 235500 T N F 2019-02-08 00:00 E01016748 Milton Keynes 028A 24481 8 21647 7 22429 7 21890 7 19373 6 19940 7
{8355F009-B768-55C5-E053-6B04A8C0D090} MK14 7BH 117500 F N L 2019-02-01 00:00 E01016718 Milton Keynes 014A 5811 2 7032 3 6351 2 5290 2 8751 3 6956 3
{8355F009-B769-55C5-E053-6B04A8C0D090} MK10 0BT 24945550 O N F 2019-01-10 00:00 E01016783 Milton Keynes 017A 23978 8 21543 7 22150 7 20486 7 25278 8 22818 7
{8355F009-B76A-55C5-E053-6B04A8C0D090} MK13 8RE 147000 F N L 2019-01-18 00:00 E01016724 Milton Keynes 013D 12503 4 10822 4 13122 4 9358 3 11901 4 11627 4
{8355F009-B76B-55C5-E053-6B04A8C0D090} HP6 5RW 325000 O N F 2019-01-31 00:00 E01017778 Chiltern 001D 28662 9 29893 10 29786 10 25229 8 32163 10 30382 10
{8355F009-B76D-55C5-E053-6B04A8C0D090} MK18 1LU 202000 S N F 2019-01-15 00:00 E01017646 Aylesbury Vale 002C 20258 7 15647 5 18225 6 17094 6 21356 7 15301 5
{8355F009-B76F-55C5-E053-6B04A8C0D090} MK18 1WD 250000 T N F 2019-01-17 00:00 E01017645 Aylesbury Vale 002B 13183 5 12624 4 17576 6 13463 5 7478 3 12449 4
{8355F009-B770-55C5-E053-6B04A8C0D090} MK3 7AN 230000 S N F 2019-02-05 00:00 E01016738 Milton Keynes 027B 8416 3 6975 3 9130 3 15435 5 9415 3 5111 2
{8355F009-B771-55C5-E053-6B04A8C0D090} HP10 9AR 321000 D N F 2019-01-31 00:00 E01017865 Wycombe 018B 32278 10 31142 10 30885 10 32340 10 31165 10 28597 9
{8355F009-B772-55C5-E053-6B04A8C0D090} RG9 6SR 2800000 O N F 2019-02-05 00:00 E01017878 Wycombe 007D 18842 6 32189 10 32579 10 29225 9 32635 10 32123 10
{8355F009-B773-55C5-E053-6B04A8C0D090} SL7 1DQ 700000 S N F 2019-02-01 00:00 E01017895 Wycombe 023A 28329 9 20972 7 21500 7 27501 9 30387 10 30081 10
{8355F009-B774-55C5-E053-6B04A8C0D090} SL7 1QX 2100000 O N F 2019-01-18 00:00 E01017896 Wycombe 023B 32818 10 32504 10 32760 10 28731 9 32378 10 32711 10
{8355F009-BA50-55C5-E053-6B04A8C0D090} PL13 2JE 285000 O N F 2019-02-15 00:00 E01018762 Cornwall 037A 10232 4 15695 5 13709 5 26228 8 10483 4 15890 5
{8355F009-BA66-55C5-E053-6B04A8C0D090} TR10 9EW 16250 O N L 2019-01-18 00:00 E01018834 Cornwall 060A 17676 6 14368 5 14406 5 29457 9 17438 6 15852 5
{8355F009-BA68-55C5-E053-6B04A8C0D090} TR11 5PU 535000 O N F 2019-01-18 00:00 E01018879 Cornwall 066A 20297 7 23554 8 18961 6 31805 10 23102 8 29412 9
{8355F009-BA6D-55C5-E053-6B04A8C0D090} TR14 0FJ 198000 S Y F 2019-01-04 00:00 E01018865 Cornwall 052A 4895 2 7527 3 4010 2 8947 3 6370 2 8377 3
{8355F009-BA6F-55C5-E053-6B04A8C0D090} TR4 8RH 5000 O N F 2019-01-31 00:00 E01018829 Cornwall 056C 10011 4 11924 4 11393 4 29426 9 16769 6 24268 8
{8355F009-BA71-55C5-E053-6B04A8C0D090} TR7 2SY 165000 T N F 2019-01-25 00:00 E01019023 Cornwall 021F 21519 7 18819 6 23237 8 30699 10 21068 7 11531 4
{8355F009-BA72-55C5-E053-6B04A8C0D090} EX23 9RQ 125000 S N F 2019-01-23 00:00 E01018943 Cornwall 002A 12514 4 15202 5 15457 5 31268 10 24972 8 14854 5
{8355F009-BA73-55C5-E053-6B04A8C0D090} TR7 3EH 700000 O N F 2019-01-22 00:00 E01019049 Cornwall 018B 13536 5 11287 4 9839 3 25576 8 19132 6 10985 4
{8355F009-BA76-55C5-E053-6B04A8C0D090} PL27 6LU 615000 D N F 2019-02-11 00:00 E01033289 Cornwall 007E 14501 5 16889 6 17738 6 25584 8 23880 8 17823 6
{8355F009-BA79-55C5-E053-6B04A8C0D090} TR26 3HX 45100 O N L 2019-01-04 00:00 E01018987 Cornwall 058C 12046 4 19101 6 15747 5 29885 10 21498 7 27643 9
{8355F009-BA7A-55C5-E053-6B04A8C0D090} PL11 2AA 100000 O N F 2019-01-18 00:00 E01018797 Cornwall 029A 5989 2 5298 2 4216 2 15493 5 2413 1 8906 3
{8355F009-BA7C-55C5-E053-6B04A8C0D090} PL28 8PY 15000 O N L 2019-01-19 00:00 E01018954 Cornwall 008A 11274 4 16985 6 21128 7 30870 10 22232 7 16329 5
{8355F009-BA7D-55C5-E053-6B04A8C0D090} TR4 9NF 1600000 O N F 2019-01-23 00:00 E01018843 Cornwall 033C 12688 4 18783 6 15821 5 29052 9 20240 7 26825 9
{8355F009-BA7E-55C5-E053-6B04A8C0D090} TR4 8QG 1020000 O N F 2019-02-04 00:00 E01018820 Cornwall 047D 13320 5 13910 5 16386 5 29138 9 18329 6 21485 7
{8355F009-BA7F-55C5-E053-6B04A8C0D090} PL15 9DY 25500 O N F 2019-02-11 00:00 E01018950 Cornwall 005E 11253 4 10279 4 8430 3 21042 7 16834 6 14332 5
{8355F009-BA80-55C5-E053-6B04A8C0D090} TR2 5BG 40000 O N F 2019-01-21 00:00 E01018849 Cornwall 048D 16230 5 15840 5 20152 7 31141 10 21819 7 21522 7
{8355F009-BA81-55C5-E053-6B04A8C0D090} LA14 1NA 47500 T N F 2019-02-06 00:00 E01019158 Barrow-in-Furness 008E 1301 1 3964 2 1620 1 2972 1 344 1 4645 2
{8355F009-BA83-55C5-E053-6B04A8C0D090} CA28 6SB 153000 D N F 2019-01-30 00:00 E01019262 Copeland 003B 24449 8 28625 9 21659 7 32007 10 15225 5 24211 8
{8355F009-BA84-55C5-E053-6B04A8C0D090} LA9 6ND 305000 O N F 2019-01-14 00:00 E01019365 South Lakeland 003C 23276 8 27110 9 25377 8 22637 7 24031 8 22001 7
{8355F009-BA85-55C5-E053-6B04A8C0D090} LA13 0JY 150000 S N F 2019-01-11 00:00 E01019179 Barrow-in-Furness 006E 28119 9 27672 9 26671 9 30770 10 11371 4 26776 9
{8355F009-BA86-55C5-E053-6B04A8C0D090} LA12 8EE 350000 T N F 2019-01-14 00:00 E01019351 South Lakeland 008D 19141 6 27945 9 30031 10 32698 10 26226 8 24448 8
{8355F009-BA88-55C5-E053-6B04A8C0D090} CA13 9YD 690000 O N F 2019-01-22 00:00 E01019090 Allerdale 007E 15065 5 28772 9 31101 10 24115 8 25116 8 29648 10
{8355F009-BA89-55C5-E053-6B04A8C0D090} CA28 8QN 72000 T N F 2019-01-25 00:00 E01019285 Copeland 003D 6060 2 8560 3 4382 2 23102 8 1899 1 3753 2
{8355F009-BA8A-55C5-E053-6B04A8C0D090} LA6 2NP 2100000 O N F 2019-01-31 00:00 E01019373 South Lakeland 009B 25655 8 30586 10 27981 9 24349 8 23782 8 28080 9
{8355F009-BA8C-55C5-E053-6B04A8C0D090} CA11 0SJ 5000 O N F 2019-02-05 00:00 E01019339 Eden 005D 15915 5 27773 9 30590 10 29318 9 31136 10 21985 7
{8355F009-BA8D-55C5-E053-6B04A8C0D090} LA9 4SX 425000 O N F 2019-01-25 00:00 E01019360 South Lakeland 004A 15960 5 16223 5 13491 5 16807 6 16460 6 27278 9
{8355F009-BA8F-55C5-E053-6B04A8C0D090} LA14 1BN 60000 T N F 2019-01-14 00:00 E01019141 Barrow-in-Furness 008A 522 1 1091 1 526 1 18371 6 238 1 1486 1
{8355F009-BA90-55C5-E053-6B04A8C0D090} LA9 5HG 1300000 O N F 2019-01-10 00:00 E01019369 South Lakeland 006D 25540 8 21788 7 17071 6 32807 10 20422 7 27711 9
{8355F009-BE62-55C5-E053-6B04A8C0D090} DE23 4DY 200000 S N F 2019-02-01 00:00 E01013545 Derby 021E 16844 6 14353 5 9936 4 27529 9 11337 4 24681 8
{8355F009-BE63-55C5-E053-6B04A8C0D090} DE23 6TE 152000 S N F 2019-02-08 00:00 E01013485 Derby 016C 3040 1 2187 1 6323 2 5214 2 8596 3 605 1
{8355F009-BE64-55C5-E053-6B04A8C0D090} DE23 8AG 3020000 O N F 2019-02-05 00:00 E01013571 Derby 023B 3641 2 4606 2 6717 3 3138 1 3210 1 2666 1
{8355F009-BE65-55C5-E053-6B04A8C0D090} DE5 8RH 300000 T N F 2019-01-18 00:00 E01019449 Amber Valley 011E 26559 9 26407 9 28907 9 27343 9 27799 9 20698 7
{8355F009-BE66-55C5-E053-6B04A8C0D090} DE22 4GJ 128000 S N F 2019-01-31 00:00 E01013552 Derby 009C 5283 2 4969 2 3820 2 16895 6 3081 1 3379 2
{8355F009-BE69-55C5-E053-6B04A8C0D090} DE22 3SX 110000 O N F 2019-01-14 00:00 E01013456 Derby 013C 1646 1 2642 1 2310 1 3962 2 803 1 2090 1
{8355F009-BE6A-55C5-E053-6B04A8C0D090} DE7 8EF 135000 O N F 2019-01-24 00:00 E01019640 Erewash 016C 31553 10 30810 10 28014 9 31943 10 19937 7 23540 8
{8355F009-BE6B-55C5-E053-6B04A8C0D090} DE3 0QH 143000 S N F 2019-01-31 00:00 E01013556 Derby 015A 30611 10 30659 10 28566 9 31265 10 18350 6 27283 9
{8355F009-BE6D-55C5-E053-6B04A8C0D090} DE4 4NR 300 O N F 2019-01-29 00:00 E01019636 Derbyshire Dales 007D 22198 7 17123 6 17864 6 28457 9 21759 7 16497 6
{8355F009-BE6E-55C5-E053-6B04A8C0D090} DE24 9NE 60000 T N F 2019-02-21 00:00 E01013588 Derby 029E 4494 2 4863 2 4039 2 2353 1 4684 2 5685 2
{8355F009-BE6F-55C5-E053-6B04A8C0D090} DE23 1JG 415000 D N F 2019-02-12 00:00 E01013488 Derby 022A 26785 9 25287 8 26195 8 28031 9 21631 7 24741 8
{8355F009-BE72-55C5-E053-6B04A8C0D090} DE22 3SE 85000 T N F 2019-01-28 00:00 E01013460 Derby 013E 9920 4 12560 4 18733 6 5013 2 8676 3 12441 4
{8355F009-BE78-55C5-E053-6B04A8C0D090} NG10 3ND 280000 O N F 2019-02-20 00:00 E01019705 Erewash 015D 23720 8 19953 7 21939 7 26753 9 20602 7 14340 5
{8355F009-BE7A-55C5-E053-6B04A8C0D090} S41 7ST 759300 D N F 2019-02-14 00:00 E01033387 Chesterfield 010F 16005 5 20751 7 22127 7 5362 2 10410 4 19725 7
{8355F009-BE7B-55C5-E053-6B04A8C0D090} S41 0JH 276000 O N F 2019-01-18 00:00 E01019798 North East Derbyshire 007A 16654 6 12692 4 14496 5 17219 6 13230 5 16298 5
{8355F009-BE7C-55C5-E053-6B04A8C0D090} DE56 0DL 245000 D N F 2019-01-11 00:00 E01019410 Amber Valley 011A 24307 8 27520 9 24945 8 29963 10 18698 6 20455 7
{8355F009-BE7E-55C5-E053-6B04A8C0D090} S42 6AW 855000 O N F 2019-02-01 00:00 E01019769 North East Derbyshire 010A 21554 7 26294 9 26699 9 30229 10 24072 8 24883 8
{8355F009-BE7F-55C5-E053-6B04A8C0D090} DE7 9JJ 185000 S N F 2019-02-05 00:00 E01019639 Erewash 016B 14261 5 9388 3 12056 4 18705 6 17213 6 6662 3
{8355F009-BE80-55C5-E053-6B04A8C0D090} DE55 2FB 247000 D N F 2019-01-14 00:00 E01019513 Bolsover 010D 20273 7 21307 7 20148 7 27576 9 12287 4 14167 5
{8355F009-BE81-55C5-E053-6B04A8C0D090} DE55 2EP 62450 T N F 2019-01-25 00:00 E01019517 Bolsover 010E 26261 8 27003 9 21327 7 27114 9 19521 6 18589 6
{8355F009-BE82-55C5-E053-6B04A8C0D090} DE7 4JB 50000 F N L 2019-01-09 00:00 E01019669 Erewash 007D 14064 5 14912 5 11919 4 26061 8 11317 4 6518 2
{8355F009-BE83-55C5-E053-6B04A8C0D090} S40 2HR 79000 T N F 2019-01-28 00:00 E01019578 Chesterfield 013D 499 1 590 1 157 1 16684 6 160 1 410 1
{8355F009-BE84-55C5-E053-6B04A8C0D090} DE7 9JE 130000 S N F 2019-01-18 00:00 E01019640 Erewash 016C 31553 10 30810 10 28014 9 31943 10 19937 7 23540 8
{8355F009-BE85-55C5-E053-6B04A8C0D090} DE4 5ND 280000 O N F 2019-01-31 00:00 E01019627 Derbyshire Dales 005E 27445 9 23414 8 24854 8 32451 10 20804 7 25009 8
{8355F009-BE88-55C5-E053-6B04A8C0D090} DE55 2FJ 290000 D N F 2019-01-31 00:00 E01019513 Bolsover 010D 20273 7 21307 7 20148 7 27576 9 12287 4 14167 5
{8355F009-BE89-55C5-E053-6B04A8C0D090} SK23 7QA 36000 O N F 2019-02-05 00:00 E01019766 High Peak 005E 23260 8 18231 6 17015 6 32070 10 17955 6 24769 8
{8355F009-BE8A-55C5-E053-6B04A8C0D090} DE23 8LL 110000 T N F 2019-01-31 00:00 E01033167 Derby 018G 666 1 531 1 849 1 15785 5 314 1 1025 1
{8355F009-BE8B-55C5-E053-6B04A8C0D090} DE7 8AZ 160000 O N F 2019-01-30 00:00 E01019672 Erewash 003E 7556 3 8691 3 6890 3 8761 3 7466 3 8677 3
{8355F009-BE8C-55C5-E053-6B04A8C0D090} DE65 6NL 225000 S N F 2019-01-31 00:00 E01019840 South Derbyshire 002A 24635 8 20834 7 20003 7 27805 9 13865 5 23787 8
{8355F009-BE8D-55C5-E053-6B04A8C0D090} NG10 2AL 115000 T N F 2019-02-19 00:00 E01019682 Erewash 012D 4792 2 6111 2 5379 2 5524 2 3971 2 5163 2
{8355F009-BE8E-55C5-E053-6B04A8C0D090} DE11 7NN 113500 O N F 2019-01-28 00:00 E01019859 South Derbyshire 007E 22175 7 19739 7 18994 6 31164 10 19580 6 12075 4
{8355F009-BE91-55C5-E053-6B04A8C0D090} DE4 2LY 625000 O N F 2019-01-11 00:00 E01019630 Derbyshire Dales 004C 23247 8 21759 7 24082 8 32133 10 27396 9 22545 7
{8355F009-B3C7-55C5-E053-6B04A8C0D090} W11 1EA 495000 F N F 2019-02-11 00:00 E01002829 Kensington and Chelsea 004A 6055 2 5953 2 5290 2 5417 2 15925 5 26510 9
{8355F009-B3C8-55C5-E053-6B04A8C0D090} SW17 7HS 60000 O N F 2019-01-25 00:00 E01004547 Wandsworth 029C 14054 5 11970 4 9731 3 13408 5 17573 6 26413 9
{8355F009-B3C9-55C5-E053-6B04A8C0D090} SE18 3PA 395000 T N F 2019-02-01 00:00 E01001681 Greenwich 020C 15871 5 14617 5 12837 4 7796 3 18238 6 25212 8
{8355F009-B3CA-55C5-E053-6B04A8C0D090} EC1M 4HF 14250000 O N F 2019-01-17 00:00 E01033490 Islington 022H 14740 5 17753 6 25893 8 22894 7 20295 7 22056 7
{8355F009-B3CB-55C5-E053-6B04A8C0D090} SE27 0BT 800000 S N F 2019-01-02 00:00 E01003080 Lambeth 027B 15676 5 15404 5 17616 6 10982 4 22367 7 27806 9
{8355F009-B3CC-55C5-E053-6B04A8C0D090} W6 7BJ 4850000 O N F 2019-01-30 00:00 E01001869 Hammersmith and Fulham 010D 19649 6 21171 7 24758 8 11543 4 19135 6 31077 10
{8355F009-B3CD-55C5-E053-6B04A8C0D090} N16 5JA 1160000 F N F 2019-01-31 00:00 E01001807 Hackney 003D 8454 3 7503 3 11046 4 13951 5 17169 6 17732 6
{8355F009-B3CE-55C5-E053-6B04A8C0D090} E5 9JW 850000 T N F 2019-02-19 00:00 E01001792 Hackney 011A 6784 3 7382 3 11517 4 2053 1 9890 4 21014 7
{8355F009-B3CF-55C5-E053-6B04A8C0D090} NW1 9RZ 16000 O N F 2019-01-10 00:00 E01000865 Camden 015B 20799 7 23464 8 26045 8 10136 4 19996 7 30190 10
{8355F009-B3D0-55C5-E053-6B04A8C0D090} SW8 3BS 47300 O N F 2019-01-31 00:00 E01003037 Lambeth 013A 4779 2 4842 2 5369 2 6026 2 1674 1 21711 7
{8355F009-B3D1-55C5-E053-6B04A8C0D090} SW17 7RH 722500 O N F 2019-02-14 00:00 E01004548 Wandsworth 029D 16560 6 15021 5 20576 7 14336 5 16228 5 26594 9
{8355F009-B3D2-55C5-E053-6B04A8C0D090} SW17 9RZ 425000 T N F 2019-02-01 00:00 E01004526 Wandsworth 035B 12185 4 10506 4 16773 6 4292 2 14572 5 25118 8
{8355F009-B3D3-55C5-E053-6B04A8C0D090} SE6 2AA 670000 D N F 2019-01-25 00:00 E01003325 Lewisham 018D 5420 2 7071 3 9575 3 5199 2 9366 3 14492 5
{8355F009-B3D4-55C5-E053-6B04A8C0D090} SW1Y 5JG 81650 O N F 2019-01-31 00:00 E01004736 Westminster 018C 24862 8 32833 10 32702 10 16515 6 32709 10 28230 9
{8355F009-B3D5-55C5-E053-6B04A8C0D090} SE24 9DH 10000 O N F 2019-01-25 00:00 E01003170 Lambeth 025E 21658 7 21062 7 26971 9 7187 3 23701 8 32259 10
{8355F009-B3D6-55C5-E053-6B04A8C0D090} N7 6QA 880000 O N F 2019-01-03 00:00 E01002731 Islington 007B 11066 4 13149 5 19620 6 2432 1 18071 6 21595 7
{8355F009-B3D7-55C5-E053-6B04A8C0D090} SE6 3JH 450000 S N F 2019-01-30 00:00 E01003318 Lewisham 020B 2640 1 5204 2 3041 1 2034 1 6051 2 17392 6
{8355F009-B3D8-55C5-E053-6B04A8C0D090} SW18 2DB 700000 T N F 2019-01-14 00:00 E01004512 Wandsworth 019B 21622 7 21567 7 20867 7 12722 4 24404 8 28723 9
{8355F009-B3D9-55C5-E053-6B04A8C0D090} SE26 5HB 250000 O N F 2019-02-18 00:00 E01003327 Lewisham 035B 17542 6 18963 6 19867 7 13215 5 22380 7 24599 8
{8355F009-B3DD-55C5-E053-6B04A8C0D090} SW17 0EA 480000 S N F 2019-02-06 00:00 E01004610 Wandsworth 033C 14415 5 16033 5 20527 7 2074 1 23157 8 27178 9
{8355F009-B3DE-55C5-E053-6B04A8C0D090} W6 8QX 2500000 O N F 2019-01-31 00:00 E01001891 Hammersmith and Fulham 018B 22811 7 29294 9 31391 10 17189 6 23251 8 15532 5
{8355F009-B3DF-55C5-E053-6B04A8C0D090} NW3 2PN 1000000 O N F 2019-02-05 00:00 E01000897 Camden 008D 30122 10 30822 10 30989 10 13725 5 32674 10 31154 10
{8355F009-B3E1-55C5-E053-6B04A8C0D090} N5 2AA 900000 O N F 2019-02-06 00:00 E01002744 Islington 009D 16228 5 14540 5 14547 5 13672 5 13266 5 25176 8
{8355F009-B3E2-55C5-E053-6B04A8C0D090} W12 8AB 815000 T N F 2019-01-14 00:00 E01001943 Hammersmith and Fulham 004A 6650 3 9442 3 9741 3 1401 1 10684 4 26495 9
{8355F009-B3E3-55C5-E053-6B04A8C0D090} SE5 8DL 680000 O N F 2019-01-09 00:00 E01004045 Southwark 027A 9397 3 5685 2 7497 3 24340 8 14852 5 18230 6
{8355F009-B3E5-55C5-E053-6B04A8C0D090} SW2 1AJ 14000 O N F 2019-01-25 00:00 E01003050 Lambeth 016B 9105 3 13413 5 19767 7 601 1 15213 5 19237 6
{8355F009-B3E6-55C5-E053-6B04A8C0D090} NW10 6AJ 21800 O N F 2019-01-25 00:00 E01001877 Hammersmith and Fulham 001D 6532 2 6516 2 6699 3 6019 2 9633 3 22569 7
{8355F009-B3E8-55C5-E053-6B04A8C0D090} SW15 6SQ 595000 O N F 2019-02-08 00:00 E01004506 Wandsworth 011A 25152 8 25163 8 30303 10 18135 6 28600 9 25710 8
{8355F009-B3E9-55C5-E053-6B04A8C0D090} NW8 9SD 52500 O N F 2019-01-21 00:00 E01004726 Westminster 003C 25261 8 26899 9 29640 10 20009 7 32628 10 28805 9
{8355F009-B3EA-55C5-E053-6B04A8C0D090} N6 5NY 290000 F N L 2019-01-11 00:00 E01002006 Haringey 035B 18324 6 19006 6 21368 7 15019 5 23437 8 31199 10
{8355F009-B3EB-55C5-E053-6B04A8C0D090} N22 6QS 3750 O N F 2019-02-01 00:00 E01002088 Haringey 013C 11266 4 14260 5 21387 7 4388 2 21957 7 8984 3
{8355F009-B3EF-55C5-E053-6B04A8C0D090} TW5 9JW 260000 T N F 2019-01-25 00:00 E01002634 Hounslow 005B 6742 3 5499 2 6546 2 7905 3 17761 6 9703 3
{8355F009-B776-55C5-E053-6B04A8C0D090} MK14 6TD 305118 D N F 2019-02-08 00:00 E01016804 Milton Keynes 009D 9002 3 7015 3 9308 3 20859 7 9873 4 7474 3
{8355F009-B779-55C5-E053-6B04A8C0D090} MK17 0HX 1845000 O N F 2019-01-31 00:00 E01017696 Aylesbury Vale 003D 32384 10 29793 10 30177 10 30205 10 31719 10 25604 8
{8355F009-B77A-55C5-E053-6B04A8C0D090} HP11 2HL 1080000 O N F 2019-01-11 00:00 E01017837 Wycombe 015B 12204 4 11192 4 14573 5 5068 2 17040 6 16809 6
{8355F009-B77B-55C5-E053-6B04A8C0D090} MK16 9HN 1005000 O N F 2019-01-25 00:00 E01016803 Milton Keynes 002F 14553 5 22468 7 28854 9 17418 6 25018 8 20417 7
{8355F009-B77C-55C5-E053-6B04A8C0D090} HP21 7SD 240000 T N F 2019-02-08 00:00 E01017634 Aylesbury Vale 015B 18084 6 18161 6 25534 8 3768 2 27255 9 15249 5
{8355F009-B77D-55C5-E053-6B04A8C0D090} SL7 2BP 625000 O N F 2019-02-08 00:00 E01017894 Wycombe 022F 32682 10 32280 10 31043 10 24601 8 31971 10 32376 10
{8355F009-B77E-55C5-E053-6B04A8C0D090} HP11 2RS 475000 O N F 2019-01-31 00:00 E01017837 Wycombe 015B 12204 4 11192 4 14573 5 5068 2 17040 6 16809 6
{8355F009-B77F-55C5-E053-6B04A8C0D090} MK10 9GQ 248400 T N F 2019-01-21 00:00 E01016783 Milton Keynes 017A 23978 8 21543 7 22150 7 20486 7 25278 8 22818 7
{8355F009-B780-55C5-E053-6B04A8C0D090} HP20 2LH 365000 O N F 2019-02-08 00:00 E01032961 Aylesbury Vale 015F 15712 5 15723 5 19670 6 5566 2 19898 7 11415 4
{8355F009-B781-55C5-E053-6B04A8C0D090} HP5 3RB 4075000 O N F 2019-01-11 00:00 E01017750 Chiltern 001A 23928 8 29980 10 28971 9 25103 8 32723 10 30062 10
{8355F009-B783-55C5-E053-6B04A8C0D090} MK3 6LL 160000 T N F 2019-02-06 00:00 E01016741 Milton Keynes 027E 7093 3 6683 3 8075 3 9931 4 11401 4 2556 1
{8355F009-B784-55C5-E053-6B04A8C0D090} MK2 3NU 190000 T N F 2019-02-05 00:00 E01016744 Milton Keynes 032C 3158 1 2827 1 5532 2 6787 3 7779 3 3468 2
{8355F009-B785-55C5-E053-6B04A8C0D090} HP21 7SE 639000 D N F 2019-01-25 00:00 E01017634 Aylesbury Vale 015B 18084 6 18161 6 25534 8 3768 2 27255 9 15249 5
{8355F009-B786-55C5-E053-6B04A8C0D090} MK11 2BA 165000 T N F 2019-01-31 00:00 E01016811 Milton Keynes 012B 2496 1 2807 1 4155 2 8531 3 12371 4 873 1
{8355F009-B787-55C5-E053-6B04A8C0D090} MK6 4HR 143000 T N F 2019-02-12 00:00 E01016842 Milton Keynes 023B 1211 1 1514 1 1773 1 2633 1 3694 2 817 1
{8355F009-B788-55C5-E053-6B04A8C0D090} HP15 6AX 45000 O N F 2019-01-31 00:00 E01032609 Chiltern 013F 27637 9 20492 7 22802 7 29594 10 25559 8 20967 7
{8355F009-B789-55C5-E053-6B04A8C0D090} HP11 2PU 630000 O N F 2019-02-08 00:00 E01017837 Wycombe 015B 12204 4 11192 4 14573 5 5068 2 17040 6 16809 6
{8355F009-B78B-55C5-E053-6B04A8C0D090} MK6 4HX 153000 T N F 2019-02-08 00:00 E01016845 Milton Keynes 018C 1595 1 1676 1 2188 1 5203 2 2580 1 942 1
{8355F009-B78D-55C5-E053-6B04A8C0D090} MK3 6AR 261000 S N F 2019-01-30 00:00 E01016834 Milton Keynes 029E 8425 3 7171 3 9315 3 21178 7 9302 3 3268 1
{8355F009-B792-55C5-E053-6B04A8C0D090} SL1 8ET 1200000 O N F 2019-01-31 00:00 E01017803 South Bucks 006A 22264 7 29489 9 29500 9 7512 3 31563 10 28880 9
{8355F009-B793-55C5-E053-6B04A8C0D090} MK6 3AD 488500 O N F 2019-02-07 00:00 E01033498 Milton Keynes 023F 5584 2 8167 3 5800 2 2390 1 8847 3 12743 4
{8355F009-B796-55C5-E053-6B04A8C0D090} MK17 0FD 3000 O N F 2019-02-05 00:00 E01017672 Aylesbury Vale 003B 27254 9 26206 8 29939 10 31097 10 26277 9 28673 9
{8355F009-B797-55C5-E053-6B04A8C0D090} HP13 6LJ 69960 O N L 2019-02-01 00:00 E01017839 Wycombe 015D 18218 6 19385 6 15143 5 9975 4 26406 9 20508 7
{8355F009-B798-55C5-E053-6B04A8C0D090} MK1 1HW 655000 O N F 2019-01-29 00:00 E01016716 Milton Keynes 030C 11200 4 9051 3 9397 3 14046 5 11998 4 10641 4
{8355F009-B799-55C5-E053-6B04A8C0D090} SL7 2DH 1663500 O N F 2019-01-18 00:00 E01017892 Wycombe 022D 32689 10 32796 10 32484 10 31582 10 32491 10 32770 10
{8355F009-B79C-55C5-E053-6B04A8C0D090} MK3 7NE 130000 F N L 2019-02-01 00:00 E01016741 Milton Keynes 027E 7093 3 6683 3 8075 3 9931 4 11401 4 2556 1
{8355F009-B79D-55C5-E053-6B04A8C0D090} MK12 5GH 285000 S N F 2019-01-11 00:00 E01016840 Milton Keynes 010D 23265 8 20684 7 25901 8 12916 4 29120 9 17867 6
{8355F009-B79E-55C5-E053-6B04A8C0D090} MK9 3FY 194119 O N L 2019-01-23 00:00 E01016768 Milton Keynes 014D 15798 5 14323 5 18635 6 7754 3 18484 6 16589 6
{8355F009-B7A1-55C5-E053-6B04A8C0D090} MK8 1AB 266250 T N F 2019-02-06 00:00 E01033525 Milton Keynes 019H 17851 6 14457 5 17469 6 18176 6 18890 6 19235 6
{8355F009-BA91-55C5-E053-6B04A8C0D090} CA22 2EU 82000 T N F 2019-02-07 00:00 E01019272 Copeland 006B 7265 3 6731 3 4707 2 28212 9 4679 2 3165 1
{8355F009-BA93-55C5-E053-6B04A8C0D090} CA14 1QU 175000 D N F 2019-02-08 00:00 E01019124 Allerdale 008E 30357 10 30826 10 25262 8 32452 10 21918 7 22182 7
{8355F009-BA94-55C5-E053-6B04A8C0D090} CA1 3TR 127500 S N L 2019-02-11 00:00 E01019251 Carlisle 004F 32375 10 28534 9 29870 10 31960 10 27583 9 29763 10
{8355F009-BA95-55C5-E053-6B04A8C0D090} LA14 2AA 69000 T N F 2019-02-08 00:00 E01019142 Barrow-in-Furness 008B 695 1 1879 1 1748 1 1479 1 353 1 2263 1
{8355F009-BA96-55C5-E053-6B04A8C0D090} LA22 0NH 285000 F N L 2019-01-31 00:00 E01019356 South Lakeland 007A 17453 6 26652 9 23167 8 31231 10 27665 9 24822 8
{8355F009-BA97-55C5-E053-6B04A8C0D090} LA10 5RQ 525000 O N F 2019-02-07 00:00 E01019384 South Lakeland 009C 18416 6 28652 9 27965 9 31682 10 30002 10 20761 7
{8355F009-BA98-55C5-E053-6B04A8C0D090} CA4 0JT 495000 O N F 2019-01-31 00:00 E01019315 Eden 002A 17191 6 23584 8 25383 8 32828 10 23922 8 19970 7
{8355F009-BA99-55C5-E053-6B04A8C0D090} LA9 6LZ 450000 O N F 2019-01-31 00:00 E01019365 South Lakeland 003C 23276 8 27110 9 25377 8 22637 7 24031 8 22001 7
{8355F009-BA9B-55C5-E053-6B04A8C0D090} CA13 0AX 203199 S N F 2019-02-07 00:00 E01019087 Allerdale 007D 31497 10 31205 10 28586 9 32735 10 25301 8 29620 10
{8355F009-BA9C-55C5-E053-6B04A8C0D090} LA9 4NF 955000 O N F 2019-02-06 00:00 E01019370 South Lakeland 003D 23740 8 24565 8 20310 7 25111 8 24300 8 23544 8
{8355F009-BA9E-55C5-E053-6B04A8C0D090} CA25 5ND 50000 T N F 2019-01-25 00:00 E01019267 Copeland 004F 3311 2 3980 2 1410 1 19475 6 1680 1 2437 1
{8355F009-BAA0-55C5-E053-6B04A8C0D090} CA14 5XJ 240000 O N F 2019-01-25 00:00 E01019268 Copeland 001A 13037 4 19334 6 15331 5 28164 9 8398 3 16764 6
{8355F009-BAA1-55C5-E053-6B04A8C0D090} LA6 2NP 2100000 O N F 2019-01-31 00:00 E01019373 South Lakeland 009B 25655 8 30586 10 27981 9 24349 8 23782 8 28080 9
{8355F009-BAA2-55C5-E053-6B04A8C0D090} CA16 6EP 625000 O N F 2019-01-22 00:00 E01019340 Eden 007E 12037 4 22561 7 16739 6 27457 9 23546 8 15324 5
{8355F009-BAA3-55C5-E053-6B04A8C0D090} CA15 7RB 500000 O N F 2019-01-25 00:00 E01019096 Allerdale 004C 19053 6 15736 5 11028 4 25405 8 18132 6 13316 5
{8355F009-BAA4-55C5-E053-6B04A8C0D090} LA22 9DG 252500 O N F 2019-01-25 00:00 E01019374 South Lakeland 001A 23496 8 22023 7 23977 8 25116 8 21275 7 14825 5
{8355F009-BAA5-55C5-E053-6B04A8C0D090} CA3 8HP 1165000 O N F 2019-01-10 00:00 E01019206 Carlisle 006B 6285 2 7136 3 4480 2 6034 2 4084 2 20834 7
{8355F009-BAA7-55C5-E053-6B04A8C0D090} CA6 6DS 50000 O N F 2019-02-07 00:00 E01019229 Carlisle 001D 10872 4 19528 6 18832 6 32488 10 20748 7 14990 5
{8355F009-BAA8-55C5-E053-6B04A8C0D090} LA12 8RB 50000 O N L 2019-02-05 00:00 E01019386 South Lakeland 007C 16067 5 27608 9 26021 8 31544 10 27991 9 22692 7
{8355F009-BAAA-55C5-E053-6B04A8C0D090} LA22 9AD 37500 O N L 2019-01-28 00:00 E01019374 South Lakeland 001A 23496 8 22023 7 23977 8 25116 8 21275 7 14825 5
{8355F009-BAAB-55C5-E053-6B04A8C0D090} CA11 7SF 220000 O N F 2019-02-08 00:00 E01019329 Eden 004D 23126 8 28451 9 25940 8 28109 9 22575 7 25792 8
{8355F009-BAAE-55C5-E053-6B04A8C0D090} LA23 1BA 418000 T N F 2019-02-05 00:00 E01019395 South Lakeland 001D 21344 7 21526 7 21150 7 28954 9 22495 7 16056 5
{8355F009-BAAF-55C5-E053-6B04A8C0D090} LA11 7HH 67000 F N L 2019-01-31 00:00 E01019354 South Lakeland 013C 19441 6 17222 6 14574 5 28548 9 14964 5 22758 7
{8355F009-BAB0-55C5-E053-6B04A8C0D090} LA8 9HN 300000 O N L 2019-02-08 00:00 E01019346 South Lakeland 003A 18879 6 22397 7 24582 8 32496 10 22982 7 17577 6
{8355F009-BAB1-55C5-E053-6B04A8C0D090} CA11 9TG 160000 O N F 2019-02-07 00:00 E01019338 Eden 002D 14896 5 26423 9 30270 10 32741 10 27600 9 20724 7
{8355F009-BAB2-55C5-E053-6B04A8C0D090} CA15 8DL 20000 O N F 2019-02-15 00:00 E01019098 Allerdale 005C 12458 4 12480 4 10885 4 20888 7 10697 4 5919 2
{8355F009-BAC8-55C5-E053-6B04A8C0D090} LA10 5AD 130000 T N F 2019-02-01 00:00 E01019385 South Lakeland 009D 19049 6 17027 6 15935 5 30100 10 17670 6 20241 7
{8355F009-BACA-55C5-E053-6B04A8C0D090} LA6 2NP 2100000 O N F 2019-01-31 00:00 E01019373 South Lakeland 009B 25655 8 30586 10 27981 9 24349 8 23782 8 28080 9
{8355F009-BAD5-55C5-E053-6B04A8C0D090} CA24 3LT 110000 O N F 2019-01-02 00:00 E01019273 Copeland 006C 18868 6 22090 7 19232 6 31405 10 10789 4 18340 6
{8355F009-BAD8-55C5-E053-6B04A8C0D090} CA8 2DZ 1300000 O N F 2019-01-11 00:00 E01019225 Carlisle 002D 14837 5 22505 7 21847 7 31513 10 23682 8 23462 8
{8355F009-BAD9-55C5-E053-6B04A8C0D090} LA23 2AB 389999 O N F 2019-01-31 00:00 E01019398 South Lakeland 002D 19854 7 21251 7 21358 7 24101 8 22929 7 7708 3
{8355F009-BADA-55C5-E053-6B04A8C0D090} CA11 8BQ 224950 O N F 2019-02-01 00:00 E01019331 Eden 004F 22999 8 19730 7 22040 7 30757 10 24113 8 9728 3
{8355F009-BADC-55C5-E053-6B04A8C0D090} CA28 7UX 130000 O N F 2019-01-29 00:00 E01019280 Copeland 002B 943 1 1890 1 117 1 1547 1 88 1 7129 3
{8355F009-BADD-55C5-E053-6B04A8C0D090} CA4 8BD 340000 S N F 2019-01-31 00:00 E01019251 Carlisle 004F 32375 10 28534 9 29870 10 31960 10 27583 9 29763 10
{8355F009-BADE-55C5-E053-6B04A8C0D090} CA25 5QH 85000 S N F 2019-01-14 00:00 E01019265 Copeland 004D 5872 2 8381 3 3153 1 14022 5 1727 1 7349 3
{8355F009-BDDF-55C5-E053-6B04A8C0D090} SR4 6NW 55000 T N F 2019-01-28 00:00 E01008757 Sunderland 015A 5051 2 4454 2 3829 2 2127 1 2676 1 11172 4
{8355F009-BDE0-55C5-E053-6B04A8C0D090} DH9 8NQ 68000 T N F 2019-01-28 00:00 E01020625 County Durham 009B 10306 4 8301 3 5611 2 15794 5 8025 3 10203 4
{8355F009-BDE1-55C5-E053-6B04A8C0D090} DH9 8NQ 90000 D N F 2019-01-28 00:00 E01020625 County Durham 009B 10306 4 8301 3 5611 2 15794 5 8025 3 10203 4
{8355F009-BDE2-55C5-E053-6B04A8C0D090} SR7 0HL 50000 F N L 2019-01-18 00:00 E01020787 County Durham 016E 8076 3 5781 2 3965 2 13595 5 7221 3 8797 3
{8355F009-BDE3-55C5-E053-6B04A8C0D090} DL3 9ER 77500 O N F 2019-02-15 00:00 E01012365 Darlington 007D 20993 7 22181 7 16888 6 4191 2 16202 5 24326 8
{8355F009-BDE5-55C5-E053-6B04A8C0D090} DH7 7EF 95000 T N F 2019-01-25 00:00 E01020714 County Durham 026D 19376 6 20013 7 15002 5 10410 4 12757 4 24963 8
{8355F009-BDE6-55C5-E053-6B04A8C0D090} DL14 9AQ 90000 S N F 2019-02-08 00:00 E01020895 County Durham 057C 4345 2 3946 2 2932 1 7004 3 2727 1 3384 2
{8355F009-BDE7-55C5-E053-6B04A8C0D090} DL12 0QE 345000 O N F 2019-02-01 00:00 E01020865 County Durham 065C 13603 5 20030 7 18880 6 22455 7 13129 4 18308 6
{8355F009-BDE8-55C5-E053-6B04A8C0D090} DL1 4DF 230000 O N F 2019-02-15 00:00 E01012340 Darlington 012D 8316 3 8137 3 9312 3 1607 1 7459 3 6367 2
{8355F009-BDEB-55C5-E053-6B04A8C0D090} DL16 7QF 135000 S N F 2019-01-25 00:00 E01020836 County Durham 052E 8726 3 8345 3 6951 3 3985 2 6250 2 8415 3
{8355F009-BDEC-55C5-E053-6B04A8C0D090} DH7 9YU 88000 T N F 2019-01-25 00:00 E01020658 County Durham 024B 6776 3 6784 3 4814 2 10079 4 2099 1 8350 3
{8355F009-BDED-55C5-E053-6B04A8C0D090} SR1 3JP 210000 O N F 2019-01-31 00:00 E01008703 Sunderland 013B 3277 1 5273 2 4161 2 1717 1 126 1 8011 3
{8355F009-BDEF-55C5-E053-6B04A8C0D090} DL4 1JD 39950 T N F 2019-01-18 00:00 E01020801 County Durham 059A 9721 3 7038 3 7491 3 3474 2 11127 4 14438 5
{8355F009-BDF0-55C5-E053-6B04A8C0D090} DL2 2BU 115000 S N F 2019-02-01 00:00 E01012338 Darlington 015A 16670 6 16775 6 13295 5 6999 3 16638 6 21677 7
{8355F009-BDF1-55C5-E053-6B04A8C0D090} DL1 5HW 120000 F N L 2019-02-22 00:00 E01033484 Darlington 008F 1586 1 2318 1 669 1 480 1 822 1 11766 4
{8355F009-BDF2-55C5-E053-6B04A8C0D090} DL14 7EW 70000 O N L 2019-01-11 00:00 E01020868 County Durham 055A 6624 3 7640 3 4206 2 1450 1 4628 2 16839 6
{8355F009-BDF3-55C5-E053-6B04A8C0D090} DH8 7EF 103000 T N F 2019-01-28 00:00 E01020650 County Durham 012A 14196 5 11143 4 11114 4 22352 7 10171 4 11514 4
{8355F009-BDF4-55C5-E053-6B04A8C0D090} DH6 2LB 185000 D N F 2019-02-01 00:00 E01020760 County Durham 034A 8326 3 9845 3 4615 2 7766 3 2730 1 11999 4
{8355F009-BDF5-55C5-E053-6B04A8C0D090} DL17 0EQ 165000 S N F 2019-02-22 00:00 E01020803 County Durham 053B 15038 5 13034 4 10371 4 12081 4 11116 4 12579 4
{8355F009-BDF6-55C5-E053-6B04A8C0D090} DH1 3RF 830000 D N F 2019-02-11 00:00 E01020713 County Durham 033E 31636 10 32606 10 32483 10 30417 10 30334 10 32769 10
{8355F009-BDF7-55C5-E053-6B04A8C0D090} DL17 0EQ 180000 D N F 2019-02-08 00:00 E01020803 County Durham 053B 15038 5 13034 4 10371 4 12081 4 11116 4 12579 4
{8355F009-BDF8-55C5-E053-6B04A8C0D090} DH9 8DY 45000 T N F 2019-01-31 00:00 E01020638 County Durham 006B 8550 3 5664 2 5461 2 12860 4 5980 2 8892 3
{8355F009-BDF9-55C5-E053-6B04A8C0D090} DH7 7RG 180000 D N F 2019-01-24 00:00 E01020715 County Durham 031C 11156 4 9050 3 6917 3 10737 4 9137 3 9693 3
{8355F009-BDFA-55C5-E053-6B04A8C0D090} DH7 7RG 155000 D N F 2019-01-24 00:00 E01020715 County Durham 031C 11156 4 9050 3 6917 3 10737 4 9137 3 9693 3
{8355F009-BDFB-55C5-E053-6B04A8C0D090} SR2 9NA 80000 T N F 2019-01-18 00:00 E01008779 Sunderland 024C 10221 4 8839 3 8166 3 6921 3 4984 2 13028 4
{8355F009-BDFD-55C5-E053-6B04A8C0D090} DL14 7BW 31500 F N L 2019-02-05 00:00 E01020872 County Durham 056C 11087 4 10352 4 8918 3 5510 2 5141 2 16544 6
{8355F009-BE00-55C5-E053-6B04A8C0D090} DH7 9FF 390000 D N F 2019-01-29 00:00 E01020660 County Durham 024D 13583 5 13921 5 11371 4 14694 5 11332 4 14012 5
{8355F009-BE01-55C5-E053-6B04A8C0D090} DH7 9FF 390000 O N F 2019-01-29 00:00 E01020660 County Durham 024D 13583 5 13921 5 11371 4 14694 5 11332 4 14012 5
{8355F009-BE03-55C5-E053-6B04A8C0D090} SR7 7TZ 115000 S N F 2019-02-01 00:00 E01020740 County Durham 017A 14665 5 14158 5 11034 4 12563 4 7333 3 11461 4
{8355F009-BE04-55C5-E053-6B04A8C0D090} SR7 7TZ 115000 S N F 2019-02-01 00:00 E01020740 County Durham 017A 14665 5 14158 5 11034 4 12563 4 7333 3 11461 4
{8355F009-BE05-55C5-E053-6B04A8C0D090} DL13 5ND 110000 O N F 2019-01-25 00:00 E01020864 County Durham 064G 15678 5 16863 6 14797 5 27213 9 16204 5 16924 6
{8355F009-BE24-55C5-E053-6B04A8C0D090} DL16 7FZ 100000 T Y F 2019-01-03 00:00 E01020820 County Durham 049C 4984 2 3852 2 3991 2 5336 2 3379 2 4833 2
{8355F009-BE2D-55C5-E053-6B04A8C0D090} SR7 9DE 39950 T N F 2019-01-09 00:00 E01033161 County Durham 021D 14725 5 15572 5 13656 5 5542 2 7235 3 12707 4
{8355F009-B3F0-55C5-E053-6B04A8C0D090} NW2 2RJ 328000 F N L 2019-01-04 00:00 E01000143 Barnet 041D 7380 3 4534 2 6262 2 5800 2 17289 6 16148 5
{8355F009-B3F1-55C5-E053-6B04A8C0D090} TW14 9BW 285000 F N L 2019-02-15 00:00 E01002590 Hounslow 022C 15491 5 18181 6 21107 7 11017 4 18459 6 10804 4
{8355F009-B3F4-55C5-E053-6B04A8C0D090} TW13 7QA 275000 F N L 2019-02-15 00:00 E01002615 Hounslow 026E 5539 2 5324 2 7011 3 10233 4 11063 4 8312 3
{8355F009-B3F5-55C5-E053-6B04A8C0D090} TW1 1DG 18200000 O N F 2019-01-18 00:00 E01003867 Richmond upon Thames 011A 22428 7 21692 7 21259 7 12292 4 28969 9 31438 10
{8355F009-B3F6-55C5-E053-6B04A8C0D090} N8 9NL 450000 T N L 2019-01-17 00:00 E01002059 Haringey 028C 13380 5 10304 4 11686 4 13195 5 17450 6 29101 9
{8355F009-B3F7-55C5-E053-6B04A8C0D090} W4 5DY 370000 F N L 2019-01-18 00:00 E01001376 Ealing 040B 11416 4 7281 3 8441 3 17584 6 19691 6 20932 7
{8355F009-B3F8-55C5-E053-6B04A8C0D090} N22 5BY 295000 F N L 2019-01-17 00:00 E01002100 Haringey 008B 6336 2 7288 3 9437 3 4141 2 16335 5 8907 3
{8355F009-B3F9-55C5-E053-6B04A8C0D090} W4 2RB 475000 F N L 2019-01-29 00:00 E01002572 Hounslow 007B 7879 3 5815 2 7673 3 12416 4 8490 3 15172 5
{8355F009-B3FB-55C5-E053-6B04A8C0D090} TW13 6UB 250000 F N L 2019-01-30 00:00 E01002610 Hounslow 028C 9741 3 8162 3 11035 4 7424 3 15017 5 8368 3
{8355F009-B3FC-55C5-E053-6B04A8C0D090} HA4 9LT 1175000 O N F 2019-02-01 00:00 E01002417 Hillingdon 008B 28823 9 28289 9 29691 10 12019 4 29836 10 29199 9
{8355F009-B3FD-55C5-E053-6B04A8C0D090} UB3 2TP 370000 S N F 2019-01-04 00:00 E01002402 Hillingdon 024C 11133 4 10274 4 11437 4 7099 3 19588 6 12411 4
{8355F009-B3FE-55C5-E053-6B04A8C0D090} N17 6LP 130000 F N L 2019-01-30 00:00 E01002089 Haringey 013D 2662 1 1073 1 2043 1 14776 5 11269 4 5560 2
{8355F009-B401-55C5-E053-6B04A8C0D090} HA6 1QL 210000 O N L 2019-01-18 00:00 E01002483 Hillingdon 002D 25352 8 22589 7 21574 7 22612 7 22704 7 28878 9
{8355F009-B402-55C5-E053-6B04A8C0D090} TW5 9PN 208000 F N L 2019-01-11 00:00 E01002633 Hounslow 005A 4802 2 3598 2 5775 2 15771 5 8977 3 8875 3
{8355F009-B406-55C5-E053-6B04A8C0D090} EN1 2NW 9000 O N L 2019-01-29 00:00 E01001405 Enfield 015A 22069 7 17645 6 24418 8 22899 7 21676 7 27570 9
{8355F009-B409-55C5-E053-6B04A8C0D090} UB6 9RU 1000 O N L 2019-01-08 00:00 E01001258 Ealing 010B 18071 6 16502 6 17384 6 19924 7 23954 8 20624 7
{8355F009-B40E-55C5-E053-6B04A8C0D090} NW2 5AP 340000 F N L 2019-01-18 00:00 E01000639 Brent 022C 12728 4 12639 4 18679 6 4948 2 27175 9 20520 7
{8355F009-B40F-55C5-E053-6B04A8C0D090} NW10 5JA 38500 O N F 2019-02-15 00:00 E01000531 Brent 033B 10593 4 8043 3 10822 4 16348 5 20245 7 21313 7
{8355F009-B411-55C5-E053-6B04A8C0D090} TW13 4PA 1000 O N L 2019-02-15 00:00 E01002616 Hounslow 027E 16858 6 16016 5 19278 6 17469 6 16383 5 11425 4
{8355F009-B412-55C5-E053-6B04A8C0D090} E3 4PG 325000 F N L 2019-02-06 00:00 E01004268 Tower Hamlets 012E 6121 2 3680 2 6345 2 19864 7 6762 3 11128 4
{8355F009-B413-55C5-E053-6B04A8C0D090} E5 8NB 247992 F N L 2019-01-25 00:00 E01001770 Hackney 010E 6158 2 5430 2 9153 3 6146 2 8154 3 13477 5
{8355F009-B415-55C5-E053-6B04A8C0D090} TW8 0AP 424000 O N L 2019-02-15 00:00 E01033082 Hounslow 003G 10592 4 9912 4 12465 4 5762 2 15374 5 20886 7
{8355F009-B416-55C5-E053-6B04A8C0D090} EN2 7AQ 336000 F N L 2019-01-30 00:00 E01001457 Enfield 010A 20056 7 17805 6 19171 6 29179 9 25147 8 24702 8
{8355F009-B417-55C5-E053-6B04A8C0D090} HA0 1BN 1320000 O N F 2019-01-30 00:00 E01000475 Brent 029C 13473 5 13267 5 19273 6 23209 8 28063 9 18822 6
{8355F009-B419-55C5-E053-6B04A8C0D090} UB6 0NY 305000 T N F 2019-02-01 00:00 E01001307 Ealing 002A 23210 8 22225 7 27549 9 17560 6 30411 10 22160 7
{8355F009-B41A-55C5-E053-6B04A8C0D090} TW7 7QQ 323500 F N L 2019-01-11 00:00 E01002673 Hounslow 020E 2294 1 1421 1 2734 1 8717 3 4688 2 6787 3
{8355F009-B41C-55C5-E053-6B04A8C0D090} HA4 8JY 5000 O N L 2019-01-21 00:00 E01002535 Hillingdon 007E 26634 9 23190 8 26887 9 16664 6 27499 9 29068 9
{8355F009-B41D-55C5-E053-6B04A8C0D090} TW8 0GR 285000 F N L 2019-01-18 00:00 E01002568 Hounslow 003D 7180 3 6014 2 11064 4 6260 2 6566 2 17222 6
{8355F009-B7A7-55C5-E053-6B04A8C0D090} HP13 5DQ 531000 O N F 2019-01-29 00:00 E01017837 Wycombe 015B 12204 4 11192 4 14573 5 5068 2 17040 6 16809 6
{8355F009-B7C1-55C5-E053-6B04A8C0D090} HP11 2FP 228000 F Y L 2019-01-11 00:00 E01017837 Wycombe 015B 12204 4 11192 4 14573 5 5068 2 17040 6 16809 6
{8355F009-B7C2-55C5-E053-6B04A8C0D090} MK3 7DT 280000 S N F 2019-01-21 00:00 E01016833 Milton Keynes 029D 28431 9 28525 9 30304 10 25177 8 27389 9 24012 8
{8355F009-B7C5-55C5-E053-6B04A8C0D090} MK18 7BB 230000 S N F 2019-01-08 00:00 E01017644 Aylesbury Vale 002A 28060 9 26956 9 27295 9 27306 9 28741 9 19951 7
{8355F009-B7C9-55C5-E053-6B04A8C0D090} MK18 3AT 300000 T N F 2019-01-17 00:00 E01017736 Aylesbury Vale 005B 22732 7 15276 5 16174 5 24334 8 24646 8 19336 6
{8355F009-B7CD-55C5-E053-6B04A8C0D090} CB4 3JL 600000 S N F 2019-01-28 00:00 E01017955 Cambridge 005A 30984 10 28342 9 32328 10 22507 7 29576 10 32562 10
{8355F009-B7CE-55C5-E053-6B04A8C0D090} PE2 5FW 148500 T N F 2019-02-07 00:00 E01015637 Peterborough 021A 1519 1 2297 1 2666 1 3218 1 1816 1 1569 1
{8355F009-B7D1-55C5-E053-6B04A8C0D090} PE28 5QU 100000 O N F 2019-02-14 00:00 E01018192 Huntingdonshire 004B 20346 7 18470 6 19953 7 23423 8 27071 9 16138 5
{8355F009-B7D2-55C5-E053-6B04A8C0D090} CB6 1RY 216000 O N F 2019-02-08 00:00 E01018027 East Cambridgeshire 003A 29025 9 27394 9 28511 9 29450 9 30686 10 25775 8
{8355F009-B7D3-55C5-E053-6B04A8C0D090} CB7 5QQ 725000 D N F 2019-01-30 00:00 E01018035 East Cambridgeshire 007B 18031 6 20187 7 21721 7 30594 10 26337 9 16164 5
{8355F009-B7D4-55C5-E053-6B04A8C0D090} PE7 3YQ 135000 S N F 2019-01-21 00:00 E01018214 Huntingdonshire 001C 15402 5 11243 4 11143 4 18685 6 20006 7 7556 3
{8355F009-B7D5-55C5-E053-6B04A8C0D090} CB21 6AN 1400000 O N F 2019-01-21 00:00 E01018288 South Cambridgeshire 017C 29835 10 26853 9 28910 9 31614 10 30437 10 29188 9
{8355F009-B7D8-55C5-E053-6B04A8C0D090} CB8 8AW 295000 S N F 2019-02-08 00:00 E01032611 East Cambridgeshire 011D 29194 9 32166 10 32491 10 30873 10 32003 10 27496 9
{8355F009-B7DA-55C5-E053-6B04A8C0D090} CB8 8AT 450000 D N F 2019-01-18 00:00 E01018019 East Cambridgeshire 011B 31590 10 30298 10 28725 9 31009 10 24073 8 25025 8
{8355F009-B7DD-55C5-E053-6B04A8C0D090} PE1 2QY 95500 F N L 2019-01-18 00:00 E01015655 Peterborough 014E 14158 5 15835 5 19582 6 3299 2 15522 5 15880 5
{8355F009-B7DE-55C5-E053-6B04A8C0D090} PE16 6DX 170000 T N F 2019-02-18 00:00 E01018102 Fenland 011B 10930 4 9891 4 9126 3 9374 3 10616 4 7218 3
{8355F009-B7DF-55C5-E053-6B04A8C0D090} PE1 1PW 275000 O N F 2019-02-15 00:00 E01015599 Peterborough 014A 4770 2 6436 2 11553 4 1760 1 8793 3 1400 1
{8355F009-B7E0-55C5-E053-6B04A8C0D090} PE29 3QS 337000 S N F 2019-01-11 00:00 E01018138 Huntingdonshire 012A 13178 5 16996 6 13646 5 12741 4 18963 6 13402 5
{8355F009-B7E2-55C5-E053-6B04A8C0D090} PE13 2PG 110000 T N F 2019-01-25 00:00 E01033112 Fenland 003I 3986 2 9043 3 6745 3 1396 1 2762 1 1742 1
{8355F009-B7E3-55C5-E053-6B04A8C0D090} PE13 2UQ 160000 D N F 2019-02-15 00:00 E01018069 Fenland 003A 15384 5 17642 6 16985 6 18334 6 6005 2 10695 4
{8355F009-B7E4-55C5-E053-6B04A8C0D090} SG8 5QN 895000 O N F 2019-02-01 00:00 E01018275 South Cambridgeshire 013D 26937 9 27713 9 29127 9 22874 7 30921 10 22505 7
{8355F009-B7E5-55C5-E053-6B04A8C0D090} PE15 8SS 130000 T N F 2019-01-31 00:00 E01018084 Fenland 005D 12357 4 13390 5 10863 4 26894 9 7143 3 10945 4
{8355F009-B7E6-55C5-E053-6B04A8C0D090} PE13 1DP 180000 F N F 2019-01-22 00:00 E01033112 Fenland 003I 3986 2 9043 3 6745 3 1396 1 2762 1 1742 1
{8355F009-B7E7-55C5-E053-6B04A8C0D090} PE2 6SN 190000 S N F 2019-02-01 00:00 E01015644 Peterborough 018B 18165 6 16966 6 19089 6 28146 9 14083 5 12795 4
{8355F009-B7E8-55C5-E053-6B04A8C0D090} PE2 8SS 165000 S N F 2019-02-15 00:00 E01015667 Peterborough 019A 9290 3 10271 4 14712 5 1253 1 7324 3 9174 3
{8355F009-BAE3-55C5-E053-6B04A8C0D090} CA13 0QE 3000 O N F 2019-02-13 00:00 E01019090 Allerdale 007E 15065 5 28772 9 31101 10 24115 8 25116 8 29648 10
{8355F009-BAE4-55C5-E053-6B04A8C0D090} CA11 8HR 330000 O N F 2019-02-15 00:00 E01019333 Eden 003C 8695 3 9831 3 7368 3 15123 5 10600 4 4234 2
{8355F009-BAE5-55C5-E053-6B04A8C0D090} CA14 2AD 145000 O N F 2019-02-22 00:00 E01019121 Allerdale 009E 1912 1 4380 2 1222 1 5445 2 1280 1 2576 1
{8355F009-BAE7-55C5-E053-6B04A8C0D090} LA23 2JS 400000 O N F 2019-02-06 00:00 E01019396 South Lakeland 002B 26961 9 21964 7 23519 8 28276 9 29506 9 21441 7
{8355F009-BAE8-55C5-E053-6B04A8C0D090} CA11 9BY 157500 T N F 2019-02-15 00:00 E01019336 Eden 003E 17200 6 18333 6 16441 6 21682 7 18669 6 11343 4
{8355F009-BAE9-55C5-E053-6B04A8C0D090} CA16 6TU 118000 O N F 2019-02-06 00:00 E01019307 Eden 006B 25163 8 23117 8 23075 8 27985 9 24956 8 13820 5
{8355F009-BAEB-55C5-E053-6B04A8C0D090} CA14 2NR 72000 O N L 2019-01-31 00:00 E01019120 Allerdale 009D 8979 3 12241 4 10393 4 8163 3 9864 4 6602 3
{8355F009-BAEC-55C5-E053-6B04A8C0D090} LA14 5PA 110000 T N F 2019-01-14 00:00 E01019167 Barrow-in-Furness 005A 11735 4 17788 6 13432 5 25697 8 3091 1 18233 6
{8355F009-BAED-55C5-E053-6B04A8C0D090} LA12 0JB 155000 T N F 2019-01-25 00:00 E01019378 South Lakeland 014A 24852 8 24333 8 22100 7 32801 10 24426 8 26902 9
{8355F009-BAEE-55C5-E053-6B04A8C0D090} LA9 4RR 165000 T N F 2019-02-01 00:00 E01019370 South Lakeland 003D 23740 8 24565 8 20310 7 25111 8 24300 8 23544 8
{8355F009-BAEF-55C5-E053-6B04A8C0D090} LA14 5AP 82500 F N F 2019-02-15 00:00 E01019171 Barrow-in-Furness 005E 6781 3 11127 4 6971 3 21207 7 1591 1 15962 5
{8355F009-BAF0-55C5-E053-6B04A8C0D090} LA18 5EE 90750 T N F 2019-02-01 00:00 E01019289 Copeland 008C 8008 3 8099 3 5496 2 32045 10 5373 2 4209 2
{8355F009-BAF1-55C5-E053-6B04A8C0D090} LA14 2HZ 55000 T N F 2019-02-18 00:00 E01019143 Barrow-in-Furness 008C 99 1 601 1 225 1 1712 1 44 1 1661 1
{8355F009-BAF3-55C5-E053-6B04A8C0D090} LA14 1AY 41000 T N F 2019-01-14 00:00 E01019142 Barrow-in-Furness 008B 695 1 1879 1 1748 1 1479 1 353 1 2263 1
{8355F009-BAF4-55C5-E053-6B04A8C0D090} LA18 4NF 650000 O N F 2019-02-21 00:00 E01019283 Copeland 008B 12225 4 19889 7 12952 4 31371 10 2340 1 8918 3
{8355F009-BAF5-55C5-E053-6B04A8C0D090} LA15 8AN 105000 S N F 2019-01-31 00:00 E01019147 Barrow-in-Furness 001D 12184 4 18178 6 14066 5 27216 9 5769 2 13104 4
{85866A64-AC7F-143F-E053-6B04A8C06A15} CO5 7PU 258000 T N F 2019-02-18 00:00 E01021694 Colchester 019D 15200 5 23254 8 16915 6 17077 6 22454 7 20485 7
{85866A64-AC80-143F-E053-6B04A8C06A15} CM18 7QJ 236000 T N F 2019-03-08 00:00 E01021862 Harlow 011A 11311 4 9927 4 11739 4 15640 5 9097 3 6640 3
{85866A64-AC81-143F-E053-6B04A8C06A15} CM9 6BW 285000 S N F 2019-02-15 00:00 E01021894 Maldon 005B 17349 6 12657 4 13781 5 25725 8 16392 5 9688 3
{85866A64-AC82-143F-E053-6B04A8C06A15} IG10 1PR 255000 F N L 2019-02-12 00:00 E01021786 Epping Forest 012A 29157 9 28037 9 27865 9 17841 6 32212 10 22634 7
{85866A64-AC85-143F-E053-6B04A8C06A15} CM2 0ND 450000 F N F 2019-02-28 00:00 E01033140 Chelmsford 010F 14057 5 13069 4 15939 5 1340 1 16716 6 28235 9
{85866A64-AC86-143F-E053-6B04A8C06A15} SS17 7JU 185000 F N L 2019-02-21 00:00 E01015987 Thurrock 002C 7637 3 7020 3 7204 3 12902 4 6874 3 4201 2
{85866A64-AC87-143F-E053-6B04A8C06A15} CM3 5ZQ 164000 T N L 2019-02-19 00:00 E01021607 Chelmsford 020C 25705 8 22354 7 24577 8 27730 9 27405 9 16916 6
{85866A64-AC89-143F-E053-6B04A8C06A15} RM17 5UH 515000 S N F 2019-02-27 00:00 E01015960 Thurrock 014A 24939 8 20120 7 24288 8 15055 5 22055 7 19285 6
{85866A64-AC8A-143F-E053-6B04A8C06A15} IG7 6ND 550000 F N L 2019-02-15 00:00 E01021749 Epping Forest 016A 23873 8 21748 7 20143 7 12952 4 28884 9 17201 6
{85866A64-AC8B-143F-E053-6B04A8C06A15} SS4 1RU 260000 T N F 2019-03-01 00:00 E01021954 Rochford 007D 15970 5 16140 5 21245 7 4411 2 19498 6 10295 4
{85866A64-AC8C-143F-E053-6B04A8C06A15} CM1 6LB 190000 F N L 2019-02-11 00:00 E01021620 Chelmsford 004D 24150 8 21386 7 23983 8 18399 6 23518 8 17467 6
{85866A64-AC8D-143F-E053-6B04A8C06A15} RM16 4TZ 211000 F N L 2019-02-21 00:00 E01015938 Thurrock 012F 5389 2 3523 2 4376 2 8404 3 10528 4 6107 2
{85866A64-AC8E-143F-E053-6B04A8C06A15} SS16 5JS 670000 D N F 2019-03-01 00:00 E01021299 Basildon 020E 24395 8 28590 9 23910 8 5364 2 23795 8 18656 6
{85866A64-AC90-143F-E053-6B04A8C06A15} CM9 5JQ 305000 S N F 2019-02-20 00:00 E01021889 Maldon 004A 9591 3 9613 3 7414 3 14158 5 15256 5 5650 2
{85866A64-AC91-143F-E053-6B04A8C06A15} RM16 2PU 283000 S N F 2019-02-08 00:00 E01015964 Thurrock 010B 20425 7 20431 7 18320 6 14298 5 21407 7 14284 5
{85866A64-AC92-143F-E053-6B04A8C06A15} CM2 7TQ 975000 D N F 2019-02-22 00:00 E01021571 Chelmsford 013E 18431 6 21393 7 26117 8 13562 5 30385 10 21493 7
{85866A64-AC93-143F-E053-6B04A8C06A15} SS14 2DJ 170000 F N L 2019-03-11 00:00 E01021319 Basildon 017E 7092 3 7985 3 6467 2 1170 1 15819 5 6015 2
{85866A64-AC95-143F-E053-6B04A8C06A15} RM15 4BJ 110000 F N L 2019-02-08 00:00 E01015921 Thurrock 015A 6684 3 8557 3 13478 5 2597 1 19861 7 7746 3
{85866A64-AC96-143F-E053-6B04A8C06A15} CM8 2DF 162500 F N L 2019-02-25 00:00 E01021416 Braintree 017B 21298 7 22876 7 19474 6 15508 5 11026 4 21087 7
{85866A64-AC97-143F-E053-6B04A8C06A15} CM1 4NX 195000 F N L 2019-02-28 00:00 E01021551 Chelmsford 011A 17311 6 24657 8 23072 8 21609 7 28360 9 16887 6
{85866A64-AC99-143F-E053-6B04A8C06A15} CM14 4AT 415000 F N L 2019-02-14 00:00 E01021439 Brentwood 007C 21446 7 18203 6 22992 8 10794 4 20223 7 23880 8
{85866A64-AC9A-143F-E053-6B04A8C06A15} CO7 9ES 230000 S N F 2019-01-21 00:00 E01021740 Colchester 017E 24360 8 17402 6 21307 7 27450 9 22961 7 18298 6
{85866A64-AC9C-143F-E053-6B04A8C06A15} SS17 0NX 350000 S N F 2019-01-11 00:00 E01015990 Thurrock 003D 12827 4 12448 4 12390 4 13711 5 17344 6 5300 2
{85866A64-AC9D-143F-E053-6B04A8C06A15} CM1 4JP 350000 T N F 2019-02-28 00:00 E01021589 Chelmsford 006C 17947 6 14737 5 15976 5 11049 4 22361 7 15834 5
{85866A64-AC9E-143F-E053-6B04A8C06A15} CM13 1YL 262000 F N L 2019-03-21 00:00 E01021453 Brentwood 005B 29525 9 27437 9 23656 8 18405 6 26326 9 26958 9
{85866A64-AC9F-143F-E053-6B04A8C06A15} RM16 2NU 444000 S N F 2019-02-13 00:00 E01015963 Thurrock 010A 6452 2 4170 2 4057 2 15803 5 13582 5 6572 3
{85866A64-ACA0-143F-E053-6B04A8C06A15} RM14 1QT 427500 S N F 2019-03-20 00:00 E01002253 Havering 019A 32507 10 32081 10 31967 10 28727 9 31592 10 26678 9
{85866A64-ACA1-143F-E053-6B04A8C06A15} CM15 0NJ 672500 D N F 2019-02-05 00:00 E01021441 Brentwood 001A 26638 9 23400 8 18517 6 28143 9 31172 10 15231 5
{85866A64-ACA2-143F-E053-6B04A8C06A15} CO3 0RY 381000 D N F 2019-03-01 00:00 E01021722 Colchester 012E 18862 6 17842 6 17870 6 21746 7 15971 5 16131 5
{85866A64-ACA3-143F-E053-6B04A8C06A15} SS9 5RY 330000 S N F 2019-02-06 00:00 E01015833 Southend-on-Sea 001A 28336 9 25242 8 24389 8 27372 9 21716 7 18613 6
{85866A64-ACA4-143F-E053-6B04A8C06A15} CM2 6RG 470000 D N F 2019-02-08 00:00 E01021546 Chelmsford 008C 23080 8 18273 6 26777 9 21458 7 29324 9 22678 7
{85866A64-ACA5-143F-E053-6B04A8C06A15} SS14 2SE 280000 T N F 2019-02-27 00:00 E01021272 Basildon 012E 10135 4 10602 4 8808 3 6768 3 15873 5 3888 2
{85866A64-ACA6-143F-E053-6B04A8C06A15} CM20 2LN 167500 F N L 2019-03-01 00:00 E01021845 Harlow 002B 11094 4 7771 3 9730 3 15203 5 11665 4 7630 3
{85866A64-AEB1-143F-E053-6B04A8C06A15} CM9 6EW 315000 S N F 2019-02-11 00:00 E01021898 Maldon 005F 18168 6 14295 5 10785 4 27929 9 16730 6 12245 4
{85866A64-AEB2-143F-E053-6B04A8C06A15} CM9 4PT 305000 T N F 2019-02-01 00:00 E01021887 Maldon 003D 23350 8 23869 8 21322 7 28248 9 27271 9 16625 6
{85866A64-AEB3-143F-E053-6B04A8C06A15} EN9 3XP 295000 F N L 2019-03-25 00:00 E01021817 Epping Forest 008E 12905 4 13646 5 14617 5 10480 4 23275 8 7501 3
{85866A64-AEB4-143F-E053-6B04A8C06A15} SS8 7DF 270000 D N F 2019-01-25 00:00 E01021500 Castle Point 012E 1311 1 717 1 339 1 5606 2 4480 2 4559 2
{85866A64-AEB5-143F-E053-6B04A8C06A15} RM16 3HD 327000 S N F 2019-03-04 00:00 E01015977 Thurrock 004A 22346 7 26613 9 21663 7 8007 3 27627 9 14784 5
{85866A64-AEB6-143F-E053-6B04A8C06A15} SS7 3LA 400000 D N F 2019-02-25 00:00 E01021510 Castle Point 002B 22348 7 20969 7 19176 6 30215 10 15534 5 14229 5
{85866A64-AEB7-143F-E053-6B04A8C06A15} CO15 4RN 265000 D N F 2019-02-28 00:00 E01021984 Tendring 010A 11388 4 11680 4 8116 3 24500 8 7724 3 5645 2
{85866A64-AEB8-143F-E053-6B04A8C06A15} CO5 9TL 475000 D N F 2019-02-22 00:00 E01021643 Colchester 019A 27601 9 28946 9 30451 10 22211 7 28914 9 17286 6
{85866A64-AEB9-143F-E053-6B04A8C06A15} CO1 2TR 148000 T N F 2019-02-25 00:00 E01021648 Colchester 007C 11046 4 12456 4 12942 4 1984 1 12984 4 23468 8
{85866A64-AEBA-143F-E053-6B04A8C06A15} SS8 7EN 188000 D N F 2019-03-08 00:00 E01021497 Castle Point 012D 14538 5 13598 5 15491 5 21146 7 18078 6 3213 1
{85866A64-AEBC-143F-E053-6B04A8C06A15} CO7 9EX 210000 S N F 2019-03-21 00:00 E01021737 Colchester 017B 22925 7 22881 7 22398 7 31210 10 18304 6 20513 7
{85866A64-AEBE-143F-E053-6B04A8C06A15} RM16 4DS 315000 S N F 2019-01-25 00:00 E01015950 Thurrock 013D 17120 6 17211 6 19001 6 12625 4 22196 7 11428 4
{85866A64-AEC2-143F-E053-6B04A8C06A15} CO9 3LH 350000 S N F 2019-03-01 00:00 E01021400 Braintree 003D 24365 8 21658 7 21328 7 27672 9 27982 9 14017 5
{85866A64-AEC3-143F-E053-6B04A8C06A15} SS3 9WD 420000 S N F 2019-03-01 00:00 E01015877 Southend-on-Sea 013B 8613 3 5923 2 6794 3 11915 4 10273 4 6874 3
{85866A64-AEC4-143F-E053-6B04A8C06A15} SS9 1DE 250000 F N L 2019-02-08 00:00 E01015846 Southend-on-Sea 011B 30087 10 25636 8 27931 9 19177 6 29251 9 26669 9
{85866A64-AEC5-143F-E053-6B04A8C06A15} CM14 4NB 607500 S N F 2019-02-07 00:00 E01021438 Brentwood 006A 31559 10 31890 10 31808 10 26150 8 32488 10 27709 9
{85866A64-AEC6-143F-E053-6B04A8C06A15} CM9 4PX 288000 T N F 2019-02-15 00:00 E01021887 Maldon 003D 23350 8 23869 8 21322 7 28248 9 27271 9 16625 6
{85866A64-AEC7-143F-E053-6B04A8C06A15} CM16 4FE 300000 F N L 2019-02-11 00:00 E01021757 Epping Forest 006B 29787 10 28672 9 30757 10 10426 4 31544 10 29333 9
{85866A64-AEC8-143F-E053-6B04A8C06A15} RM15 5LR 300000 T N F 2019-02-22 00:00 E01015972 Thurrock 006C 13691 5 11041 4 14807 5 9180 3 16249 5 9448 3
{85866A64-AEC9-143F-E053-6B04A8C06A15} CO12 4WL 176000 D N F 2019-02-28 00:00 E01022004 Tendring 004D 14048 5 15068 5 14549 5 11392 4 6122 2 8064 3
{85866A64-AECA-143F-E053-6B04A8C06A15} SS5 6HG 290000 S N F 2019-02-08 00:00 E01021944 Rochford 001C 27306 9 28072 9 25457 8 20737 7 31033 10 9861 4
{85866A64-AECB-143F-E053-6B04A8C06A15} CM9 4NR 323500 S N F 2019-02-08 00:00 E01021886 Maldon 003C 23349 8 21830 7 17080 6 28749 9 24086 8 15697 5
{85866A64-AECC-143F-E053-6B04A8C06A15} CM8 1TZ 225000 T N F 2019-02-11 00:00 E01021427 Braintree 016C 10404 4 11664 4 11053 4 9413 3 12465 4 5187 2
{85866A64-AECD-143F-E053-6B04A8C06A15} SS1 2SY 450000 S N F 2019-02-11 00:00 E01015892 Southend-on-Sea 016D 28552 9 24107 8 26125 8 18595 6 27022 9 19373 6
{85866A64-AECE-143F-E053-6B04A8C06A15} IG1 2UW 375000 T N F 2019-03-08 00:00 E01003739 Redbridge 031C 14512 5 8555 3 15658 5 9756 3 25908 8 22039 7
{85866A64-AECF-143F-E053-6B04A8C06A15} RM16 6AP 210000 F N L 2019-02-15 00:00 E01033613 Thurrock 019D 29837 10 30295 10 30798 10 23593 8 29863 10 24058 8
{85866A64-AED0-143F-E053-6B04A8C06A15} CO1 2PF 318000 D N F 2019-02-01 00:00 E01021648 Colchester 007C 11046 4 12456 4 12942 4 1984 1 12984 4 23468 8
{85866A64-AED1-143F-E053-6B04A8C06A15} CO12 5FG 255000 D N F 2019-03-13 00:00 E01022002 Tendring 004C 16473 6 15310 5 12675 4 26099 8 11942 4 9408 3
{85866A64-AED2-143F-E053-6B04A8C06A15} SS7 3AD 312500 S N F 2019-02-15 00:00 E01021512 Castle Point 004D 28781 9 27306 9 27752 9 27897 9 27508 9 14976 5
{85866A64-AED3-143F-E053-6B04A8C06A15} CO15 6PY 58000 F N L 2019-01-24 00:00 E01022027 Tendring 016D 1294 1 3683 2 1076 1 598 1 1515 1 4282 2
{85866A64-AED4-143F-E053-6B04A8C06A15} CM20 2PN 334500 S N F 2019-03-08 00:00 E01021853 Harlow 003E 12925 4 14216 5 14571 5 4608 2 15773 5 10573 4
{85866A64-AA55-143F-E053-6B04A8C06A15} SS15 6QR 385000 D N F 2019-02-18 00:00 E01021275 Basildon 014B 17300 6 18736 6 12667 4 9199 3 19596 6 11949 4
{85866A64-AA56-143F-E053-6B04A8C06A15} CO2 8BS 278000 S N F 2019-03-21 00:00 E01021664 Colchester 016C 14329 5 16323 5 15897 5 14863 5 16310 5 13043 4
{85866A64-AA57-143F-E053-6B04A8C06A15} SS9 1QA 475000 S N F 2019-03-01 00:00 E01015847 Southend-on-Sea 011C 23770 8 19789 7 20554 7 17068 6 24753 8 21293 7
{85866A64-AA59-143F-E053-6B04A8C06A15} RM1 4EF 600000 T N F 2019-03-07 00:00 E01002339 Havering 008D 29691 10 29664 10 30230 10 19751 7 30293 10 20315 7
{85866A64-AA5A-143F-E053-6B04A8C06A15} EN9 1TA 340000 T N F 2019-02-15 00:00 E01021808 Epping Forest 008B 12226 4 10077 4 14236 5 16316 5 22428 7 5108 2
{85866A64-AA5B-143F-E053-6B04A8C06A15} IG10 3DE 455000 T N F 2019-03-01 00:00 E01021785 Epping Forest 014D 14744 5 11692 4 12526 4 10792 4 20278 7 9065 3
{85866A64-AA5C-143F-E053-6B04A8C06A15} RM15 6EB 347500 S N F 2019-02-14 00:00 E01015930 Thurrock 007D 12172 4 13319 5 14322 5 9226 3 21746 7 7972 3
{85866A64-AA5D-143F-E053-6B04A8C06A15} CM18 7BJ 413000 D N F 2019-02-19 00:00 E01021823 Harlow 006D 6901 3 5765 2 6141 2 10620 4 7576 3 3880 2
{85866A64-AA5E-143F-E053-6B04A8C06A15} SS2 6XJ 288000 S N F 2019-03-08 00:00 E01015865 Southend-on-Sea 002C 15188 5 16044 5 14365 5 21553 7 16035 5 6796 3
{85866A64-AA5F-143F-E053-6B04A8C06A15} SS17 9AD 525000 D N F 2019-02-14 00:00 E01015943 Thurrock 001A 20575 7 23382 8 23197 8 14259 5 26304 9 11565 4
{85866A64-AA60-143F-E053-6B04A8C06A15} IG7 5BX 635000 T N F 2019-03-01 00:00 E01021752 Epping Forest 016D 23975 8 22798 7 29130 9 10134 4 30647 10 18023 6
{85866A64-AA62-143F-E053-6B04A8C06A15} IG10 3LP 300000 T N F 2019-02-18 00:00 E01021771 Epping Forest 013A 5221 2 4339 2 5344 2 7090 3 12147 4 3244 1
{85866A64-AA63-143F-E053-6B04A8C06A15} SS9 3EB 425000 D N F 2019-03-18 00:00 E01015821 Southend-on-Sea 005B 20310 7 17351 6 16442 6 18209 6 17519 6 16759 6
{85866A64-AA64-143F-E053-6B04A8C06A15} SS16 4BE 265000 T N F 2019-02-15 00:00 E01021295 Basildon 020B 9576 3 9463 3 11000 4 2142 1 16504 6 6803 3
{85866A64-AA65-143F-E053-6B04A8C06A15} E4 9SG 630000 S N F 2019-03-06 00:00 E01004386 Waltham Forest 003C 25868 8 27219 9 24801 8 17798 6 25645 8 22079 7
{85866A64-AA66-143F-E053-6B04A8C06A15} CM11 2BZ 435000 S N F 2019-02-21 00:00 E01021244 Basildon 004A 31412 10 30900 10 31642 10 17370 6 30269 10 28755 9
{85866A64-AA67-143F-E053-6B04A8C06A15} SS17 0RE 338000 S N F 2019-03-15 00:00 E01015950 Thurrock 013D 17120 6 17211 6 19001 6 12625 4 22196 7 11428 4
{85866A64-AA68-143F-E053-6B04A8C06A15} IG6 2UT 445000 T N F 2019-03-12 00:00 E01003702 Redbridge 006E 17235 6 14595 5 19884 7 10579 4 23633 8 17649 6
{85866A64-AA69-143F-E053-6B04A8C06A15} SS3 9DX 220000 T N F 2019-03-13 00:00 E01015917 Southend-on-Sea 017D 3667 2 3614 2 3062 1 5121 2 6131 2 1923 1
{85866A64-AA6A-143F-E053-6B04A8C06A15} SS13 3DH 470000 D N F 2019-03-11 00:00 E01021310 Basildon 022B 11590 4 16381 5 16347 5 961 1 16266 5 9371 3
{85866A64-AA6B-143F-E053-6B04A8C06A15} SS16 5HU 422000 S N F 2019-02-28 00:00 E01021299 Basildon 020E 24395 8 28590 9 23910 8 5364 2 23795 8 18656 6
{85866A64-AA6C-143F-E053-6B04A8C06A15} CB11 3QR 277000 T N F 2019-03-01 00:00 E01022073 Uttlesford 003D 29380 9 25334 8 27979 9 20515 7 26932 9 20222 7
{85866A64-AA6D-143F-E053-6B04A8C06A15} SS3 8AH 311500 D N F 2019-03-04 00:00 E01015915 Southend-on-Sea 017B 24412 8 26015 8 25772 8 8167 3 20177 7 21633 7
{85866A64-AA6E-143F-E053-6B04A8C06A15} SS16 5DU 123000 T N F 2019-03-01 00:00 E01021296 Basildon 020C 12969 4 12018 4 11559 4 3827 2 19569 6 11857 4
{85866A64-AA6F-143F-E053-6B04A8C06A15} CM12 0AR 867000 D N F 2019-02-12 00:00 E01021247 Basildon 003E 31165 10 27843 9 29719 10 23462 8 28815 9 26921 9
{85866A64-AA70-143F-E053-6B04A8C06A15} CM2 6RT 475000 D N F 2019-02-28 00:00 E01021548 Chelmsford 013A 29046 9 25713 8 27049 9 22822 7 30525 10 24905 8
{85866A64-AA71-143F-E053-6B04A8C06A15} EN9 1LE 430000 S N F 2019-02-28 00:00 E01021813 Epping Forest 007B 24014 8 27320 9 26380 9 10253 4 27608 9 10840 4
{85866A64-AA72-143F-E053-6B04A8C06A15} CO6 3LJ 255000 T N F 2019-03-08 00:00 E01021729 Colchester 003C 24483 8 19484 6 15744 5 29791 10 19873 7 23768 8
{85866A64-AA73-143F-E053-6B04A8C06A15} CM3 5TT 290000 S N F 2019-03-18 00:00 E01021611 Chelmsford 019A 29960 10 29487 9 27772 9 27886 9 31237 10 19667 6
{85866A64-AA74-143F-E053-6B04A8C06A15} RM20 3JT 186500 F N L 2019-02-25 00:00 E01016012 Thurrock 015C 10896 4 12837 4 17782 6 4963 2 17870 6 10767 4
{85866A64-AA75-143F-E053-6B04A8C06A15} CM1 6YS 320000 S N F 2019-02-08 00:00 E01021621 Chelmsford 007B 23198 8 23852 8 26567 9 15829 5 30199 10 15917 5
{85866A64-AA76-143F-E053-6B04A8C06A15} SS3 8AF 455000 D N F 2019-03-15 00:00 E01015915 Southend-on-Sea 017B 24412 8 26015 8 25772 8 8167 3 20177 7 21633 7
{85866A64-AA77-143F-E053-6B04A8C06A15} IG7 5LR 290000 F N L 2019-03-15 00:00 E01021766 Epping Forest 017C 26195 8 24488 8 29626 10 6783 3 30679 10 23618 8
{85866A64-AA78-143F-E053-6B04A8C06A15} RM17 6BH 315000 T N F 2019-03-01 00:00 E01015967 Thurrock 014B 22370 7 21402 7 22563 7 15374 5 25199 8 15398 5
{85866A64-ACA7-143F-E053-6B04A8C06A15} CO6 1TN 138000 F N L 2019-02-21 00:00 E01021377 Braintree 014A 23647 8 20705 7 18820 6 27648 9 26115 8 14542 5
{85866A64-ACA9-143F-E053-6B04A8C06A15} CO3 3AZ 465000 S N F 2019-02-08 00:00 E01021653 Colchester 013C 32188 10 31314 10 30533 10 30257 10 27899 9 32057 10
{85866A64-ACAA-143F-E053-6B04A8C06A15} SS7 5HJ 340000 S N F 2019-02-22 00:00 E01021524 Castle Point 007E 23277 8 19283 6 19957 7 15394 5 22022 7 14430 5
{85866A64-ACAB-143F-E053-6B04A8C06A15} CO15 2RQ 150000 D N F 2019-02-01 00:00 E01021990 Tendring 018C 1514 1 1637 1 1100 1 6831 3 1626 1 595 1
{85866A64-ACAC-143F-E053-6B04A8C06A15} CO2 7RU 212500 T N F 2019-03-08 00:00 E01021684 Colchester 011B 23032 8 17507 6 22837 7 17107 6 20487 7 22699 7
{85866A64-ACAD-143F-E053-6B04A8C06A15} CM2 9QX 298000 T N F 2019-03-20 00:00 E01021563 Chelmsford 014B 11098 4 10378 4 7605 3 10686 4 13558 5 13871 5
{8CAC1318-B9FC-0253-E053-6B04A8C08E51} S2 1WY 110000 T N F 2019-06-10 00:00 E01008015 Sheffield 039D 1162 1 2225 1 777 1 5453 2 1271 1 319 1
{8CAC1318-B9FD-0253-E053-6B04A8C08E51} S11 8AA 164000 F N L 2019-04-12 00:00 E01008109 Sheffield 042B 7254 3 6974 3 11841 4 3822 2 7524 3 9593 3
{8CAC1318-B9FE-0253-E053-6B04A8C08E51} DN3 1FE 148500 S N F 2019-05-21 00:00 E01007624 Doncaster 007D 28636 9 26447 9 24791 8 17650 6 23198 8 22191 7
{8CAC1318-B9FF-0253-E053-6B04A8C08E51} S66 3RY 210000 D N F 2019-05-28 00:00 E01032927 Rotherham 012F 13877 5 15947 5 11078 4 8977 3 6644 3 17831 6
{8CAC1318-BA00-0253-E053-6B04A8C08E51} S72 8WP 100000 S N F 2019-05-24 00:00 E01007331 Barnsley 003B 12603 4 13051 4 9288 3 7928 3 8500 3 9788 3
{8CAC1318-BA01-0253-E053-6B04A8C08E51} S9 4PR 105000 T N L 2019-05-22 00:00 E01007907 Sheffield 032A 9719 3 7658 3 10031 4 16490 6 12400 4 5428 2
{8CAC1318-BA02-0253-E053-6B04A8C08E51} S13 7DP 64000 F N L 2019-06-10 00:00 E01007970 Sheffield 049D 270 1 666 1 30 1 317 1 23 1 2506 1
{8CAC1318-BA03-0253-E053-6B04A8C08E51} S73 0DH 66000 F N L 2019-03-15 00:00 E01032925 Barnsley 026G 9285 3 10883 4 10686 4 14007 5 1648 1 5891 2
{8CAC1318-BA04-0253-E053-6B04A8C08E51} DN5 0RE 75000 T N F 2019-06-12 00:00 E01007511 Doncaster 010E 2201 1 1684 1 2923 1 1436 1 4410 2 874 1
{8CAC1318-BA05-0253-E053-6B04A8C08E51} S1 4EY 135000 F N L 2019-05-17 00:00 E01033265 Sheffield 074C 10947 4 24154 8 21735 7 698 1 8259 3 22355 7
{8CAC1318-BA06-0253-E053-6B04A8C08E51} DN4 7DQ 92500 F N L 2019-01-25 00:00 E01007527 Doncaster 027B 23327 8 25883 8 24165 8 12315 4 17405 6 26914 9
{8CAC1318-BA07-0253-E053-6B04A8C08E51} DN5 0EN 120000 S N F 2019-04-29 00:00 E01007508 Doncaster 010B 1508 1 1623 1 1786 1 451 1 4540 2 524 1
{8CAC1318-BA08-0253-E053-6B04A8C08E51} S70 6AX 130000 S N F 2019-05-31 00:00 E01007415 Barnsley 013C 7917 3 9785 3 7267 3 1395 1 4999 2 12003 4
{8CAC1318-BA09-0253-E053-6B04A8C08E51} S11 9PR 367000 T N L 2019-04-25 00:00 E01007928 Sheffield 055B 32441 10 31687 10 30841 10 28498 9 28998 9 32561 10
{8CAC1318-BA0A-0253-E053-6B04A8C08E51} S6 3RR 194000 S N F 2019-06-06 00:00 E01008155 Sheffield 024D 14859 5 12510 4 15996 5 16351 5 12525 4 10800 4
{8CAC1318-BA0B-0253-E053-6B04A8C08E51} S26 5QR 185000 T N F 2019-06-11 00:00 E01007751 Rotherham 033C 14770 5 14155 5 13200 5 8654 3 9243 3 14474 5
{8CAC1318-BA0C-0253-E053-6B04A8C08E51} DN9 3PL 173000 S N F 2019-06-14 00:00 E01007599 Doncaster 026A 23888 8 25776 8 21441 7 17061 6 18899 6 20689 7
{8CAC1318-BA0D-0253-E053-6B04A8C08E51} S11 9SF 525000 S N L 2019-05-31 00:00 E01007928 Sheffield 055B 32441 10 31687 10 30841 10 28498 9 28998 9 32561 10
{8CAC1318-BA0E-0253-E053-6B04A8C08E51} S6 4QB 157000 S N F 2019-05-31 00:00 E01007992 Sheffield 021B 21154 7 22066 7 21428 7 17295 6 11244 4 24459 8
{8CAC1318-BA0F-0253-E053-6B04A8C08E51} S64 9NA 38000 T N F 2019-05-31 00:00 E01007577 Doncaster 031F 249 1 883 1 482 1 520 1 312 1 202 1
{8CAC1318-BA10-0253-E053-6B04A8C08E51} S2 3HG 157000 T N F 2019-05-17 00:00 E01008101 Sheffield 043D 3673 2 3996 2 2831 1 4826 2 2277 1 5467 2
{8CAC1318-BA11-0253-E053-6B04A8C08E51} S35 2EP 172500 S N F 2019-04-30 00:00 E01007890 Sheffield 004C 25917 8 27762 9 24592 8 16163 5 16679 6 19287 6
{8CAC1318-BA12-0253-E053-6B04A8C08E51} S72 7LJ 105000 S N F 2019-05-22 00:00 E01007332 Barnsley 006C 2908 1 5847 2 3972 2 1931 1 414 1 1199 1
{8CAC1318-BA13-0253-E053-6B04A8C08E51} S10 1FG 180000 T N L 2019-06-18 00:00 E01007962 Sheffield 029C 19368 6 17242 6 19736 7 24797 8 6459 2 24004 8
{8CAC1318-BA15-0253-E053-6B04A8C08E51} S8 0SU 138000 F N L 2019-05-03 00:00 E01007829 Sheffield 059B 18870 6 19466 6 17093 6 11672 4 15393 5 23050 8
{8CAC1318-BA17-0253-E053-6B04A8C08E51} S62 6FL 158000 S N F 2019-06-19 00:00 E01007776 Rotherham 008B 7127 3 6142 2 6502 2 3723 2 5748 2 8077 3
{8CAC1318-BA18-0253-E053-6B04A8C08E51} S74 0LU 159000 T N F 2019-06-17 00:00 E01007457 Barnsley 029E 6545 2 7910 3 5813 2 5125 2 2107 1 5878 2
{8CAC1318-BA19-0253-E053-6B04A8C08E51} DN3 2FR 145000 S N F 2019-05-24 00:00 E01033259 Doncaster 014E 21123 7 21162 7 24460 8 11922 4 9475 3 18778 6
{8CAC1318-BA1A-0253-E053-6B04A8C08E51} S36 9QS 300000 D N F 2019-06-17 00:00 E01007427 Barnsley 024A 21458 7 18937 6 16294 5 27173 9 16746 6 17864 6
{8CAC1318-BA1C-0253-E053-6B04A8C08E51} S73 9HN 168000 S N F 2019-05-31 00:00 E01007351 Barnsley 020D 6896 3 9374 3 7348 3 2241 1 5363 2 3471 2
{8CAC1318-BC26-0253-E053-6B04A8C08E51} S75 1FE 209950 D Y F 2019-05-17 00:00 E01007381 Barnsley 012C 23905 8 22968 7 17514 6 23470 8 11876 4 22177 7
{8CAC1318-BC27-0253-E053-6B04A8C08E51} DN2 5SS 105000 S N F 2019-05-31 00:00 E01007650 Doncaster 015B 10921 4 12610 4 8547 3 8575 3 5628 2 10177 4
{8CAC1318-BC28-0253-E053-6B04A8C08E51} S74 9LS 115000 S N F 2019-01-18 00:00 E01007388 Barnsley 029A 16032 5 17787 6 12434 4 11469 4 7756 3 15568 5
{8CAC1318-BC29-0253-E053-6B04A8C08E51} DN6 0BF 109995 T Y F 2019-04-30 00:00 E01007489 Doncaster 002C 12952 4 11649 4 13976 5 15128 5 8240 3 7818 3
{8CAC1318-BC2A-0253-E053-6B04A8C08E51} S71 4GG 219995 D Y F 2019-05-24 00:00 E01007439 Barnsley 001D 20520 7 21928 7 15440 5 6266 2 17567 6 17401 6
{8CAC1318-BC2C-0253-E053-6B04A8C08E51} S2 4DD 68750 F Y L 2019-05-22 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC2D-0253-E053-6B04A8C08E51} S8 8NA 217000 S Y F 2019-05-30 00:00 E01007827 Sheffield 064A 22051 7 25854 8 21430 7 7499 3 15185 5 24825 8
{8CAC1318-BC2E-0253-E053-6B04A8C08E51} DN4 5EU 75000 T N F 2019-05-17 00:00 E01007644 Doncaster 019A 13814 5 16416 5 14453 5 3889 2 14541 5 10337 4
{8CAC1318-BC2F-0253-E053-6B04A8C08E51} S2 4DD 75750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC30-0253-E053-6B04A8C08E51} S10 2LW 300000 F Y L 2019-05-16 00:00 E01008104 Sheffield 036E 18528 6 17417 6 24903 8 13247 5 14188 5 18949 6
{8CAC1318-BC31-0253-E053-6B04A8C08E51} DN6 7FJ 129995 S Y F 2019-05-30 00:00 E01007466 Doncaster 009C 1164 1 1506 1 1436 1 322 1 3279 1 308 1
{8CAC1318-BC32-0253-E053-6B04A8C08E51} S2 4DD 68500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC33-0253-E053-6B04A8C08E51} S2 4DD 72500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC35-0253-E053-6B04A8C08E51} S2 4DD 72500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC36-0253-E053-6B04A8C08E51} S2 4DD 72500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC37-0253-E053-6B04A8C08E51} S2 4DD 66500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC38-0253-E053-6B04A8C08E51} S2 4DD 71750 F Y L 2019-05-11 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC39-0253-E053-6B04A8C08E51} S2 4DD 66500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3A-0253-E053-6B04A8C08E51} S2 4DD 66500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3B-0253-E053-6B04A8C08E51} S2 4FS 69750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3C-0253-E053-6B04A8C08E51} S2 4DD 67750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3D-0253-E053-6B04A8C08E51} S2 4DD 67750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3E-0253-E053-6B04A8C08E51} S2 4DD 67500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC3F-0253-E053-6B04A8C08E51} S2 4DD 71750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC40-0253-E053-6B04A8C08E51} S2 4DD 65500 F Y L 2019-05-11 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC41-0253-E053-6B04A8C08E51} S2 4DD 65500 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC42-0253-E053-6B04A8C08E51} S2 4DD 73750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC43-0253-E053-6B04A8C08E51} S2 4DD 75750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BC44-0253-E053-6B04A8C08E51} S2 4DD 71750 F Y L 2019-05-20 00:00 E01033273 Sheffield 042F 12083 4 19521 6 28255 9 6888 3 9767 3 4021 2
{8CAC1318-BE39-0253-E053-6B04A8C08E51} SW12 0DT 450000 F N L 2019-06-14 00:00 E01004473 Wandsworth 026A 22310 7 19712 7 24163 8 14189 5 17465 6 32655 10
{8CAC1318-BE3B-0253-E053-6B04A8C08E51} SW16 2RJ 300000 F N L 2019-05-31 00:00 E01003154 Lambeth 030E 8914 3 6554 2 7553 3 15102 5 9763 3 18743 6
{8CAC1318-BE3C-0253-E053-6B04A8C08E51} SW12 9DS 437000 F N L 2019-05-31 00:00 E01004474 Wandsworth 021B 16534 6 15734 5 21772 7 7773 3 17721 6 22958 7
{8CAC1318-BE3D-0253-E053-6B04A8C08E51} SE18 6TJ 452000 F N L 2019-06-11 00:00 E01033736 Greenwich 004H 23883 8 32496 10 32745 10 7444 3 20960 7 24563 8
{8CAC1318-BE3E-0253-E053-6B04A8C08E51} SW12 9PL 499999 F N L 2019-05-31 00:00 E01004473 Wandsworth 026A 22310 7 19712 7 24163 8 14189 5 17465 6 32655 10
{8CAC1318-BE3F-0253-E053-6B04A8C08E51} SE18 3AT 220000 F N L 2019-05-31 00:00 E01001700 Greenwich 011D 7478 3 6462 2 10226 4 8491 3 11202 4 12202 4
{8CAC1318-BE40-0253-E053-6B04A8C08E51} SW12 0NU 370000 F N L 2019-06-14 00:00 E01003156 Lambeth 023B 15573 5 15120 5 17502 6 17108 6 9305 3 31009 10
{8CAC1318-BE41-0253-E053-6B04A8C08E51} SE5 0XH 370000 F N L 2019-06-21 00:00 E01003926 Southwark 021F 7330 3 5254 2 10527 4 6929 3 10582 4 18955 6
{8CAC1318-BE42-0253-E053-6B04A8C08E51} SE28 0NE 425000 T N F 2019-06-07 00:00 E01033740 Greenwich 002F 14071 5 10245 4 15436 5 7524 3 20561 7 26399 9
{8CAC1318-BE44-0253-E053-6B04A8C08E51} SW16 5BY 405000 F N L 2019-04-29 00:00 E01003145 Lambeth 034D 11635 4 15110 5 19155 6 7931 3 16040 5 21267 7
{8CAC1318-BE45-0253-E053-6B04A8C08E51} SE4 1HS 470000 F N F 2019-04-23 00:00 E01003275 Lewisham 014C 7727 3 5081 2 7066 3 21449 7 10199 4 16583 6
{8CAC1318-BE46-0253-E053-6B04A8C08E51} SE15 6HR 260000 F N L 2019-04-12 00:00 E01004012 Southwark 019C 9413 3 6942 3 10569 4 13684 5 15887 5 21252 7
{8CAC1318-BE47-0253-E053-6B04A8C08E51} SE17 2RB 310000 F N L 2019-06-07 00:00 E01003964 Southwark 015A 5759 2 7268 3 8021 3 1587 1 15704 5 15091 5
{8CAC1318-BE48-0253-E053-6B04A8C08E51} SE18 6JG 200000 F N L 2019-04-25 00:00 E01033735 Greenwich 004G 9152 3 11023 4 18556 6 2385 1 10493 4 20804 7
{8CAC1318-BE49-0253-E053-6B04A8C08E51} SE22 8BZ 353000 F N L 2019-05-20 00:00 E01004045 Southwark 027A 9397 3 5685 2 7497 3 24340 8 14852 5 18230 6
{8CAC1318-BE4A-0253-E053-6B04A8C08E51} SE5 7GD 190000 F N L 2019-06-06 00:00 E01003916 Southwark 020B 10919 4 9217 3 15610 5 8197 3 15830 5 24176 8
{8CAC1318-BE4C-0253-E053-6B04A8C08E51} TW1 4NF 652000 T N F 2019-06-20 00:00 E01003884 Richmond upon Thames 016C 29942 10 27722 9 27198 9 15378 5 31317 10 32604 10
{8CAC1318-BE4D-0253-E053-6B04A8C08E51} SE19 1LS 397500 F N L 2019-05-24 00:00 E01003948 Southwark 033D 16743 6 16049 5 18666 6 7249 3 18374 6 31477 10
{8CAC1318-BE4E-0253-E053-6B04A8C08E51} SE28 0GJ 241000 F N L 2019-06-06 00:00 E01033742 Greenwich 007F 20945 7 19844 7 27920 9 19491 6 22041 7 24139 8
{8CAC1318-BE4F-0253-E053-6B04A8C08E51} SW15 2SR 440000 F N L 2019-05-31 00:00 E01004507 Wandsworth 014E 26239 8 22845 7 30249 10 26781 9 30369 10 27933 9
{8CAC1318-BE50-0253-E053-6B04A8C08E51} SE1 3BP 700000 F N L 2019-05-29 00:00 E01032803 Southwark 003J 17880 6 18347 6 28160 9 17793 6 11084 4 30293 10
{8CAC1318-BE51-0253-E053-6B04A8C08E51} SE12 8JP 375000 F N L 2019-06-07 00:00 E01001658 Greenwich 034C 15955 5 12696 4 18519 6 14592 5 23354 8 22262 7
{8CAC1318-BE52-0253-E053-6B04A8C08E51} SE9 3NZ 350000 T N F 2019-04-10 00:00 E01001596 Greenwich 032A 25312 8 24812 8 23260 8 20000 7 24571 8 19475 6
{8CAC1318-BE53-0253-E053-6B04A8C08E51} SE2 9NU 230000 F N L 2019-06-14 00:00 E01001578 Greenwich 003D 9697 3 7328 3 10349 4 6101 2 18493 6 13609 5
{8CAC1318-BE54-0253-E053-6B04A8C08E51} TW12 1DW 615000 S N F 2019-05-08 00:00 E01003813 Richmond upon Thames 019B 30765 10 30944 10 26966 9 22208 7 31051 10 32705 10
{8CAC1318-BE56-0253-E053-6B04A8C08E51} TW2 5BE 459950 F N L 2019-06-17 00:00 E01003898 Richmond upon Thames 015A 27203 9 22641 7 27077 9 16846 6 28787 9 32000 10
{8CAC1318-BE57-0253-E053-6B04A8C08E51} SE22 8QP 875000 T N F 2019-06-20 00:00 E01004072 Southwark 030E 25533 8 26019 8 26939 9 12498 4 28838 9 31643 10
{8CAC1318-BE58-0253-E053-6B04A8C08E51} SE28 0LU 255000 F N L 2019-05-10 00:00 E01033739 Greenwich 002E 15162 5 11720 4 21087 7 18894 6 19522 6 25501 8
{8CAC1318-BE59-0253-E053-6B04A8C08E51} SE18 5EL 200000 F N L 2019-05-29 00:00 E01001710 Greenwich 005D 6464 2 3640 2 4850 2 16633 6 10463 4 9849 3
{8CAC1318-C051-0253-E053-6B04A8C08E51} SE3 0TG 487350 F N L 2019-06-05 00:00 E01003200 Lewisham 009B 22667 7 27917 9 31167 10 7037 3 27269 9 31772 10
{8CAC1318-C052-0253-E053-6B04A8C08E51} E16 2SG 519000 F N L 2019-04-26 00:00 E01033577 Newham 037E 15262 5 14172 5 22784 7 19262 6 21403 7 19376 6
{8CAC1318-C054-0253-E053-6B04A8C08E51} SW17 8AU 700000 F N L 2019-05-07 00:00 E01004481 Wandsworth 029A 18761 6 17594 6 21179 7 11613 4 17340 6 31137 10
{8CAC1318-C055-0253-E053-6B04A8C08E51} E16 2PT 714000 F N L 2019-05-24 00:00 E01033577 Newham 037E 15262 5 14172 5 22784 7 19262 6 21403 7 19376 6
{8CAC1318-C056-0253-E053-6B04A8C08E51} E16 2JW 499000 F N L 2019-06-07 00:00 E01033577 Newham 037E 15262 5 14172 5 22784 7 19262 6 21403 7 19376 6
{8CAC1318-C057-0253-E053-6B04A8C08E51} SE22 9EA 485000 F N L 2019-04-26 00:00 E01003955 Southwark 028B 15889 5 15398 5 14737 5 16291 5 11223 4 27367 9
{8A78B2B0-1D2B-5CB0-E053-6B04A8C0F504} NE38 8HN 80000 T N F 2019-05-03 00:00 E01008847 Sunderland 017C 3112 1 2660 1 2747 1 17269 6 1907 1 1682 1
{8A78B2B0-1D2C-5CB0-E053-6B04A8C0F504} NE39 2LH 88000 T N F 2019-03-22 00:00 E01008183 Gateshead 024A 7454 3 7102 3 7266 3 4059 2 5275 2 8249 3
{8A78B2B0-1D2D-5CB0-E053-6B04A8C0F504} NE11 9FG 75500 F N L 2019-02-08 00:00 E01008209 Gateshead 007A 10554 4 8846 3 8960 3 22247 7 2817 1 11829 4
{8A78B2B0-1D2E-5CB0-E053-6B04A8C0F504} NE28 7JP 92500 F N L 2019-03-29 00:00 E01008576 North Tyneside 029D 6765 3 4703 2 5817 2 11003 4 6336 2 8792 3
{8A78B2B0-1D2F-5CB0-E053-6B04A8C0F504} NE29 7AE 72000 F N L 2019-04-29 00:00 E01008484 North Tyneside 023C 2439 1 1829 1 1437 1 8523 3 2768 1 2560 1
{8A78B2B0-1D30-5CB0-E053-6B04A8C0F504} NE29 7AH 50000 F N L 2019-05-10 00:00 E01008484 North Tyneside 023C 2439 1 1829 1 1437 1 8523 3 2768 1 2560 1
{8A78B2B0-1D32-5CB0-E053-6B04A8C0F504} DH4 6HX 61500 S N F 2019-05-07 00:00 E01008752 Sunderland 033C 5147 2 4580 2 3126 1 8487 3 5007 2 3115 1
{8A78B2B0-1D33-5CB0-E053-6B04A8C0F504} NE31 1HZ 140000 S N F 2019-04-09 00:00 E01008652 South Tyneside 009B 2699 1 3302 2 2176 1 4111 2 355 1 5663 2
{8A78B2B0-1D34-5CB0-E053-6B04A8C0F504} NE29 6TP 113200 T N F 2019-05-10 00:00 E01008544 North Tyneside 027D 864 1 673 1 426 1 494 1 1323 1 6646 3
{8A78B2B0-1D35-5CB0-E053-6B04A8C0F504} NE27 0DG 127000 S N F 2019-05-10 00:00 E01008501 North Tyneside 018D 25440 8 25168 8 23644 8 18855 6 16062 5 25346 8
{8A78B2B0-1D36-5CB0-E053-6B04A8C0F504} DH4 7TZ 300000 D N F 2019-05-17 00:00 E01008797 Sunderland 030C 22701 7 24811 8 16513 6 16838 6 14536 5 26073 8
{8A78B2B0-1D37-5CB0-E053-6B04A8C0F504} NE28 7JS 270000 D N F 2019-04-26 00:00 E01008576 North Tyneside 029D 6765 3 4703 2 5817 2 11003 4 6336 2 8792 3
{8A78B2B0-1D38-5CB0-E053-6B04A8C0F504} NE6 5DT 117000 F N L 2019-04-18 00:00 E01008360 Newcastle upon Tyne 018C 21835 7 19647 6 23505 8 17739 6 6063 2 31157 10
{8A78B2B0-1D39-5CB0-E053-6B04A8C0F504} NE32 4SF 90000 S N F 2019-05-03 00:00 E01008632 South Tyneside 017B 5825 2 4516 2 3525 2 12913 4 3144 1 7375 3
{8A78B2B0-1D3A-5CB0-E053-6B04A8C0F504} NE2 2BP 156000 F N L 2019-04-18 00:00 E01008369 Newcastle upon Tyne 013B 26386 9 31641 10 31416 10 12300 4 9730 3 30308 10
{8A78B2B0-1D3B-5CB0-E053-6B04A8C0F504} NE16 5BE 245000 T N L 2019-04-25 00:00 E01008275 Gateshead 017E 29115 9 32472 10 28981 9 30642 10 14495 5 29569 10
{8A78B2B0-1D3C-5CB0-E053-6B04A8C0F504} NE15 7TL 148000 S N F 2019-03-22 00:00 E01008416 Newcastle upon Tyne 025C 14353 5 13712 5 12346 4 4344 2 13161 5 15580 5
{8A78B2B0-1D3D-5CB0-E053-6B04A8C0F504} NE3 1BQ 432500 T N F 2019-05-06 00:00 E01008418 Newcastle upon Tyne 005C 30892 10 32655 10 31897 10 11710 4 30418 10 32822 10
{8A78B2B0-1D3E-5CB0-E053-6B04A8C0F504} NE9 6PW 99950 T N F 2019-04-30 00:00 E01008189 Gateshead 023A 2552 1 3212 1 1959 1 5968 2 1293 1 4664 2
{8A78B2B0-1D3F-5CB0-E053-6B04A8C0F504} DH5 9BE 142000 D N F 2019-05-09 00:00 E01008748 Sunderland 035E 6931 3 8458 3 3256 1 5860 2 5051 2 11672 4
{8A78B2B0-1D40-5CB0-E053-6B04A8C0F504} NE29 7HL 115950 T N F 2019-05-17 00:00 E01008482 North Tyneside 023B 2392 1 2447 1 1366 1 5692 2 1250 1 3647 2
{8A78B2B0-1D41-5CB0-E053-6B04A8C0F504} NE34 8LS 88000 T N F 2019-04-29 00:00 E01008692 South Tyneside 019E 5943 2 5848 2 3778 2 13531 5 3526 2 5677 2
{8A78B2B0-1D42-5CB0-E053-6B04A8C0F504} NE29 7DY 80000 F N L 2019-04-26 00:00 E01008492 North Tyneside 023E 4998 2 3881 2 3373 2 3896 2 7081 3 5154 2
{8A78B2B0-1D43-5CB0-E053-6B04A8C0F504} NE9 5HJ 246000 S N F 2019-04-26 00:00 E01008237 Gateshead 016D 30305 10 31400 10 26733 9 17930 6 18290 6 31122 10
{8A78B2B0-1D44-5CB0-E053-6B04A8C0F504} SR2 9HZ 235000 D N L 2019-04-30 00:00 E01008780 Sunderland 024D 6349 2 5078 2 3124 1 14797 5 2750 1 13105 4
{8A78B2B0-1D45-5CB0-E053-6B04A8C0F504} NE28 8QQ 83000 F N L 2019-04-12 00:00 E01008535 North Tyneside 030B 13384 5 13824 5 10136 4 2463 1 13346 5 15684 5
{8A78B2B0-1D46-5CB0-E053-6B04A8C0F504} NE17 7JL 52000 T N F 2019-04-17 00:00 E01008186 Gateshead 024B 3479 2 2912 1 1340 1 7717 3 3478 2 6437 2
{8A78B2B0-1D47-5CB0-E053-6B04A8C0F504} NE27 0AY 95000 S N F 2019-05-21 00:00 E01008565 North Tyneside 007C 14346 5 13958 5 14343 5 9036 3 13334 5 15383 5
{8A78B2B0-1D48-5CB0-E053-6B04A8C0F504} DH4 5AG 94000 T N F 2019-04-18 00:00 E01008754 Sunderland 034F 5325 2 5630 2 2980 1 3249 1 3023 1 8989 3
{8A78B2B0-1D49-5CB0-E053-6B04A8C0F504} NE16 4BT 246000 D N F 2019-05-17 00:00 E01008264 Gateshead 009C 30610 10 29220 9 25112 8 24455 8 19974 7 30201 10
{8A78B2B0-1D4A-5CB0-E053-6B04A8C0F504} NE16 3DF 80000 F N L 2019-03-29 00:00 E01008262 Gateshead 009A 7898 3 6646 3 4935 2 19708 7 2899 1 11479 4
{8A78B2B0-1D4B-5CB0-E053-6B04A8C0F504} NE5 1DY 131000 S N L 2019-05-10 00:00 E01008328 Newcastle upon Tyne 015E 24916 8 28122 9 18044 6 16170 5 13790 5 23105 8
{8A78B2B0-1F6F-5CB0-E053-6B04A8C0F504} NE3 2XG 160000 S N F 2019-04-30 00:00 E01008312 Newcastle upon Tyne 001F 26094 8 23266 8 22203 7 16331 5 18268 6 26378 9
{8A78B2B0-1F70-5CB0-E053-6B04A8C0F504} NE3 3UL 363500 S N F 2019-04-29 00:00 E01008421 Newcastle upon Tyne 006F 31549 10 28924 9 28612 9 25695 8 23222 8 32739 10
{8A78B2B0-1F71-5CB0-E053-6B04A8C0F504} NE15 7SJ 196000 T N F 2019-05-13 00:00 E01008386 Newcastle upon Tyne 021F 14193 5 11613 4 10599 4 22879 7 9862 4 10282 4
{8A78B2B0-1F72-5CB0-E053-6B04A8C0F504} NE2 2SP 585000 T N F 2019-04-12 00:00 E01008369 Newcastle upon Tyne 013B 26386 9 31641 10 31416 10 12300 4 9730 3 30308 10
{8A78B2B0-1F73-5CB0-E053-6B04A8C0F504} SR4 0ND 80000 S N F 2019-05-17 00:00 E01008811 Sunderland 014B 13209 5 11249 4 9865 4 13143 5 6425 2 12363 4
{8A78B2B0-1F74-5CB0-E053-6B04A8C0F504} NE3 2FF 126000 T N L 2019-04-26 00:00 E01008294 Newcastle upon Tyne 001A 26280 9 22386 7 23326 8 24691 8 16777 6 24384 8
{8A78B2B0-1F75-5CB0-E053-6B04A8C0F504} DH4 4TS 90000 T N F 2019-04-05 00:00 E01008796 Sunderland 032D 12257 4 10743 4 10381 4 5655 2 8713 3 11229 4
{8A78B2B0-1F76-5CB0-E053-6B04A8C0F504} NE30 3ET 289950 S N F 2019-04-18 00:00 E01008497 North Tyneside 010C 32185 10 32187 10 29672 10 22973 7 21059 7 30504 10
{8A78B2B0-1F77-5CB0-E053-6B04A8C0F504} NE15 6TN 132000 S N F 2019-02-27 00:00 E01033547 Newcastle upon Tyne 025E 649 1 633 1 797 1 3855 2 1428 1 206 1
{8A78B2B0-1F78-5CB0-E053-6B04A8C0F504} NE32 3TW 110000 T N F 2019-04-18 00:00 E01008600 South Tyneside 007A 1089 1 581 1 934 1 5455 2 1679 1 998 1
{8A78B2B0-1F79-5CB0-E053-6B04A8C0F504} NE27 0ED 83000 T N F 2019-04-26 00:00 E01008570 North Tyneside 011D 21743 7 18979 6 22397 7 31212 10 13089 4 14898 5
{8A78B2B0-1F7A-5CB0-E053-6B04A8C0F504} NE16 5QJ 167500 S N F 2019-04-15 00:00 E01008270 Gateshead 017C 30235 10 29722 10 25673 8 26213 8 15693 5 28485 9
{8A78B2B0-1F7B-5CB0-E053-6B04A8C0F504} NE28 9XZ 187000 D N L 2019-04-26 00:00 E01008466 North Tyneside 021B 29577 10 30492 10 25634 8 28183 9 18571 6 28298 9
{8A78B2B0-1F7C-5CB0-E053-6B04A8C0F504} NE15 8DQ 93500 T N F 2019-04-18 00:00 E01008385 Newcastle upon Tyne 021E 3138 1 3851 2 3906 2 560 1 2673 1 3459 2
{8A78B2B0-1F7D-5CB0-E053-6B04A8C0F504} NE40 3DH 150000 T N F 2019-05-17 00:00 E01008249 Gateshead 001D 22421 7 21737 7 16643 6 14894 5 10709 4 26831 9
{8A78B2B0-1F7E-5CB0-E053-6B04A8C0F504} NE38 0LT 93000 T N F 2019-05-10 00:00 E01008868 Sunderland 020B 25948 8 23825 8 24086 8 26873 9 18743 6 20407 7
{8A78B2B0-1F7F-5CB0-E053-6B04A8C0F504} SR2 8JJ 40000 T N F 2019-04-12 00:00 E01008737 Sunderland 016D 810 1 687 1 393 1 114 1 1011 1 7618 3
{8A78B2B0-1F80-5CB0-E053-6B04A8C0F504} NE28 8TE 129950 S N L 2019-04-18 00:00 E01008534 North Tyneside 025C 21442 7 20343 7 17266 6 25474 8 14894 5 21449 7
{8A78B2B0-1F81-5CB0-E053-6B04A8C0F504} NE34 0SF 202000 T N F 2019-04-30 00:00 E01008673 South Tyneside 003A 26235 8 26625 9 19600 6 20477 7 14037 5 29567 10
{8A78B2B0-1F82-5CB0-E053-6B04A8C0F504} SR4 8BG 113000 T N F 2019-05-03 00:00 E01008813 Sunderland 014C 3778 2 3774 2 2502 1 6278 2 2118 1 3226 1
{8A78B2B0-1F83-5CB0-E053-6B04A8C0F504} NE37 2ND 146000 S N F 2019-04-26 00:00 E01008856 Sunderland 007B 19409 6 19592 6 12317 4 29075 9 8827 3 20279 7
{8A78B2B0-1F85-5CB0-E053-6B04A8C0F504} SR2 9DQ 154000 S N L 2019-05-22 00:00 E01008780 Sunderland 024D 6349 2 5078 2 3124 1 14797 5 2750 1 13105 4
{8A78B2B0-1F86-5CB0-E053-6B04A8C0F504} NE33 4RJ 85000 T N F 2019-05-02 00:00 E01008590 South Tyneside 011A 2677 1 2760 1 1413 1 738 1 2923 1 7272 3
{8A78B2B0-1F87-5CB0-E053-6B04A8C0F504} NE6 5UE 179500 S N F 2019-04-26 00:00 E01008363 Newcastle upon Tyne 012E 24663 8 23144 8 20204 7 22518 7 13165 5 27260 9
{8A78B2B0-1F88-5CB0-E053-6B04A8C0F504} NE38 8RX 257000 D N F 2019-04-05 00:00 E01008861 Sunderland 025B 13353 5 12000 4 7852 3 23348 8 10947 4 11950 4
{8A78B2B0-1F89-5CB0-E053-6B04A8C0F504} SR4 7RX 149950 T N F 2019-05-16 00:00 E01008760 Sunderland 018A 23330 8 21983 7 20471 7 15105 5 11527 4 23999 8
{8A78B2B0-1F8A-5CB0-E053-6B04A8C0F504} SR4 7LY 30000 T N L 2019-05-10 00:00 E01008832 Sunderland 018C 11344 4 10973 4 11282 4 4972 2 7077 3 11199 4
{8A78B2B0-1F8B-5CB0-E053-6B04A8C0F504} NE38 7HF 119950 S N F 2019-04-26 00:00 E01008848 Sunderland 019C 17790 6 19478 6 12431 4 11541 4 14916 5 18038 6
{8A78B2B0-1F8C-5CB0-E053-6B04A8C0F504} NE34 9HB 185000 D N F 2019-04-24 00:00 E01008605 South Tyneside 016B 9344 3 8293 3 6943 3 7357 3 6912 3 10222 4
{8A78B2B0-1F8D-5CB0-E053-6B04A8C0F504} NE33 3AG 85000 S N F 2019-03-12 00:00 E01008676 South Tyneside 003C 10251 4 8792 3 7519 3 9146 3 4510 2 15023 5
{8A78B2B0-1F8E-5CB0-E053-6B04A8C0F504} NE5 5JU 129000 S N F 2019-04-25 00:00 E01008441 Newcastle upon Tyne 031A 5826 2 5753 2 4187 2 10622 4 2386 1 7088 3
{8A78B2B0-1F8F-5CB0-E053-6B04A8C0F504} NE13 6HJ 97000 S N F 2019-04-30 00:00 E01008582 North Tyneside 004E 13897 5 10348 4 12301 4 15610 5 12451 4 9165 3
{8A78B2B0-1F90-5CB0-E053-6B04A8C0F504} SR6 7DT 122000 S N F 2019-05-17 00:00 E01008687 South Tyneside 018E 16700 6 10383 4 8523 3 26470 9 14872 5 20076 7
{85866A64-ACAE-143F-E053-6B04A8C06A15} SS8 0HF 357500 D N F 2019-02-19 00:00 E01021501 Castle Point 011D 14681 5 12209 4 12794 4 20250 7 15058 5 7418 3
{85866A64-ACAF-143F-E053-6B04A8C06A15} CM4 9AG 498000 D N F 2019-03-08 00:00 E01021459 Brentwood 002C 32201 10 31920 10 30181 10 18542 6 31865 10 30219 10
{85866A64-ACB0-143F-E053-6B04A8C06A15} CM1 2RL 190000 F N L 2019-02-15 00:00 E01033138 Chelmsford 009F 27058 9 24682 8 25781 8 16392 5 25995 8 24558 8
{85866A64-ACB2-143F-E053-6B04A8C06A15} CM18 7RN 140000 F N L 2019-03-13 00:00 E01021859 Harlow 010C 8483 3 6988 3 7551 3 7730 3 10544 4 5476 2
{85866A64-ACB3-143F-E053-6B04A8C06A15} CM17 0HR 345000 T N F 2019-03-19 00:00 E01021854 Harlow 001A 16040 5 15503 5 14436 5 10355 4 11724 4 12800 4
{85866A64-ACB6-143F-E053-6B04A8C06A15} CB11 4PF 200000 S N F 2019-02-27 00:00 E01022061 Uttlesford 003A 29300 9 29397 9 32263 10 25259 8 32651 10 29587 10
{85866A64-ACB7-143F-E053-6B04A8C06A15} RM3 0SL 390000 T N F 2019-03-19 00:00 E01002298 Havering 007D 22608 7 18694 6 20816 7 18788 6 26407 9 17385 6
{85866A64-ACB8-143F-E053-6B04A8C06A15} CO15 1BP 169000 T N F 2019-02-08 00:00 E01022036 Tendring 017C 10441 4 13918 5 12175 4 5246 2 9941 4 6681 3
{85866A64-ACB9-143F-E053-6B04A8C06A15} SS9 5RY 350000 D N F 2019-02-28 00:00 E01015833 Southend-on-Sea 001A 28336 9 25242 8 24389 8 27372 9 21716 7 18613 6
{85866A64-ACBB-143F-E053-6B04A8C06A15} SS8 9EA 220000 D N F 2019-02-22 00:00 E01021484 Castle Point 011A 14562 5 14943 5 12297 4 11254 4 20691 7 5025 2
{85866A64-ACBC-143F-E053-6B04A8C06A15} CM6 2NE 780000 D N F 2019-02-21 00:00 E01022092 Uttlesford 004A 25008 8 28509 9 26463 9 25527 8 31322 10 21542 7
{85866A64-ACBE-143F-E053-6B04A8C06A15} SS11 7EF 365950 T N F 2019-03-11 00:00 E01021334 Basildon 005A 21388 7 20964 7 18908 6 12047 4 19484 6 14391 5
{85866A64-ACC1-143F-E053-6B04A8C06A15} CO4 9UN 665995 D N F 2019-03-11 00:00 E01021671 Colchester 002D 22155 7 19436 6 15416 5 23811 8 14026 5 27648 9
{85866A64-ACC2-143F-E053-6B04A8C06A15} CO15 2RZ 70000 D N F 2019-02-18 00:00 E01021990 Tendring 018C 1514 1 1637 1 1100 1 6831 3 1626 1 595 1
{85866A64-ACC3-143F-E053-6B04A8C06A15} SS4 3HX 255000 S N F 2019-02-25 00:00 E01021929 Rochford 002D 32073 10 30772 10 28278 9 29962 10 31121 10 23829 8
{85866A64-ACC4-143F-E053-6B04A8C06A15} SS13 1RT 280000 T N F 2019-02-22 00:00 E01021308 Basildon 011D 16676 6 19383 6 19510 6 9418 3 17374 6 6538 2
{85866A64-ACC5-143F-E053-6B04A8C06A15} CM2 6XD 210000 F N L 2019-02-14 00:00 E01021547 Chelmsford 008D 25778 8 25894 8 28741 9 20458 7 30373 10 26522 9
{85866A64-ACC6-143F-E053-6B04A8C06A15} CO1 2BL 200000 T N F 2019-03-08 00:00 E01021686 Colchester 011D 5142 2 4162 2 6526 2 5571 2 5536 2 10119 4
{85866A64-ACC7-143F-E053-6B04A8C06A15} CM22 7JB 553500 D N F 2019-03-08 00:00 E01022059 Uttlesford 009A 24749 8 27480 9 30026 10 14338 5 31260 10 23266 8
{85866A64-ACC8-143F-E053-6B04A8C06A15} CM15 9NR 242000 T N F 2019-02-07 00:00 E01021461 Brentwood 004D 12860 4 10654 4 12049 4 10625 4 17909 6 6332 2
{85866A64-ACCA-143F-E053-6B04A8C06A15} CM2 6UB 163000 F N L 2019-01-25 00:00 E01021547 Chelmsford 008D 25778 8 25894 8 28741 9 20458 7 30373 10 26522 9
{85866A64-ACCB-143F-E053-6B04A8C06A15} CM4 9EG 610000 S N F 2019-02-22 00:00 E01021458 Brentwood 002B 29814 10 24676 8 26869 9 19759 7 29379 9 26933 9
{85866A64-ACCC-143F-E053-6B04A8C06A15} CM2 0TL 540000 S N F 2019-02-15 00:00 E01021577 Chelmsford 012B 28378 9 26088 8 25194 8 13262 5 30271 10 29274 9
{85866A64-ACCD-143F-E053-6B04A8C06A15} CO15 2NN 214000 D N F 2019-01-31 00:00 E01021990 Tendring 018C 1514 1 1637 1 1100 1 6831 3 1626 1 595 1
{85866A64-ACCF-143F-E053-6B04A8C06A15} CO16 8RY 290000 D N F 2019-02-15 00:00 E01022045 Tendring 018D 1253 1 2878 1 1500 1 278 1 2165 1 5405 2
{85866A64-ACD0-143F-E053-6B04A8C06A15} CO15 3HP 177000 T N F 2019-02-19 00:00 E01021970 Tendring 016A 419 1 833 1 435 1 486 1 643 1 1351 1
{85866A64-ACD1-143F-E053-6B04A8C06A15} CO15 4LE 158000 S N F 2019-03-13 00:00 E01022041 Tendring 010D 10272 4 10967 4 6448 2 12709 4 8289 3 5681 2
{85866A64-AED5-143F-E053-6B04A8C06A15} CM2 6YZ 385000 S N F 2019-02-06 00:00 E01021545 Chelmsford 008B 29733 10 28677 9 30068 10 20979 7 28283 9 21924 7
{85866A64-AED6-143F-E053-6B04A8C06A15} CO6 2SB 258000 D N F 2019-03-21 00:00 E01021410 Braintree 006E 15398 5 10005 4 10628 4 27988 9 15396 5 11591 4
{85866A64-AED8-143F-E053-6B04A8C06A15} CM2 8PX 249000 T N F 2019-01-21 00:00 E01021552 Chelmsford 017A 22220 7 19856 7 19300 6 19943 7 24141 8 13976 5
{85866A64-AED9-143F-E053-6B04A8C06A15} CB11 4FG 875000 D N F 2019-02-14 00:00 E01022075 Uttlesford 001B 32811 10 32586 10 32740 10 30019 10 31890 10 32488 10
{85866A64-AEDA-143F-E053-6B04A8C06A15} CO16 9DT 100000 S N F 2019-02-15 00:00 E01022018 Tendring 007E 10827 4 13702 5 8241 3 16597 6 6683 3 7691 3
{85866A64-AEDB-143F-E053-6B04A8C06A15} CM7 1XF 345000 D N F 2019-03-22 00:00 E01021373 Braintree 011D 18595 6 15456 5 14374 5 21445 7 21176 7 11426 4
{85866A64-AEDC-143F-E053-6B04A8C06A15} CM2 9NF 246500 T N F 2019-03-08 00:00 E01021585 Chelmsford 015D 31111 10 30824 10 30373 10 27324 9 31460 10 17808 6
{85866A64-AEDD-143F-E053-6B04A8C06A15} CM2 8BN 385000 S N F 2019-02-15 00:00 E01021559 Chelmsford 017E 30472 10 30985 10 29952 10 30250 10 30234 10 26742 9
{85866A64-AEDE-143F-E053-6B04A8C06A15} CM17 9WH 270000 T N F 2019-02-25 00:00 E01021828 Harlow 005E 29026 9 28726 9 31670 10 30082 10 26399 9 19091 6
{85866A64-AEDF-143F-E053-6B04A8C06A15} CO13 0PT 235000 S N F 2019-02-14 00:00 E01022009 Tendring 008E 14233 5 14656 5 12164 4 24084 8 9331 3 11562 4
{85866A64-AEE0-143F-E053-6B04A8C06A15} CO15 6AE 189995 F N L 2019-01-25 00:00 E01022027 Tendring 016D 1294 1 3683 2 1076 1 598 1 1515 1 4282 2
{85866A64-AEE2-143F-E053-6B04A8C06A15} RM19 1SA 235000 T N F 2019-01-25 00:00 E01016012 Thurrock 015C 10896 4 12837 4 17782 6 4963 2 17870 6 10767 4
{85866A64-AEE3-143F-E053-6B04A8C06A15} SS2 4LQ 180000 F N L 2019-02-28 00:00 E01015875 Southend-on-Sea 006G 3734 2 3197 1 2319 1 4807 2 5037 2 5011 2
{85866A64-AEE4-143F-E053-6B04A8C06A15} SS3 0BG 288000 S N F 2019-03-08 00:00 E01021923 Rochford 010D 26100 8 23882 8 21637 7 22909 7 26157 8 12612 4
{85866A64-AEE6-143F-E053-6B04A8C06A15} IG7 5NF 195000 F N L 2019-02-20 00:00 E01021764 Epping Forest 017A 9597 3 5799 2 10191 4 11898 4 16888 6 6711 3
{85866A64-AEE7-143F-E053-6B04A8C06A15} CM11 1ND 329000 S N F 2019-02-15 00:00 E01021604 Chelmsford 018D 24703 8 27375 9 30125 10 14388 5 32261 10 24088 8
{85866A64-AEE8-143F-E053-6B04A8C06A15} CM2 7LL 367500 S N F 2019-02-15 00:00 E01021564 Chelmsford 013D 29223 9 27928 9 23489 8 17722 6 29895 10 24257 8
{85866A64-AEE9-143F-E053-6B04A8C06A15} SS3 9UP 430000 D N F 2019-03-14 00:00 E01015881 Southend-on-Sea 013F 19853 7 15780 5 14860 5 15631 5 21976 7 13696 5
{85866A64-AEEA-143F-E053-6B04A8C06A15} CO4 0AT 155000 F N L 2019-03-22 00:00 E01021702 Colchester 022D 20886 7 18375 6 24539 8 12433 4 19004 6 16201 5
{85866A64-AEEB-143F-E053-6B04A8C06A15} SS8 7SN 195000 S N F 2019-03-05 00:00 E01021497 Castle Point 012D 14538 5 13598 5 15491 5 21146 7 18078 6 3213 1
{85866A64-AEEC-143F-E053-6B04A8C06A15} RM16 6BY 225000 F N L 2019-03-01 00:00 E01015982 Thurrock 019A 27368 9 27768 9 30257 10 15036 5 25345 8 26687 9
{85866A64-AEED-143F-E053-6B04A8C06A15} CM8 3RP 267000 S N F 2019-02-08 00:00 E01021360 Braintree 012A 14463 5 11036 4 11786 4 19396 6 15817 5 5774 2
{85866A64-AEEE-143F-E053-6B04A8C06A15} CO5 7EA 222000 T N F 2019-03-20 00:00 E01021657 Colchester 016A 21358 7 18702 6 18764 6 20922 7 19172 6 19681 6
{85866A64-AEEF-143F-E053-6B04A8C06A15} CO7 6AW 450000 S N F 2019-03-06 00:00 E01021656 Colchester 001B 21899 7 20953 7 21581 7 22662 7 29877 10 24087 8
{85866A64-AEF1-143F-E053-6B04A8C06A15} CB9 7DX 202800 T N F 2019-03-08 00:00 E01021375 Braintree 001A 25740 8 25001 8 19676 6 26546 9 23642 8 22541 7
{85866A64-AEF3-143F-E053-6B04A8C06A15} CM6 1LX 415000 S N F 2019-03-18 00:00 E01022058 Uttlesford 008A 20459 7 27552 9 28262 9 29918 10 29027 9 25412 8
{85866A64-AEF5-143F-E053-6B04A8C06A15} SS3 0EQ 202500 F N L 2019-03-22 00:00 E01021923 Rochford 010D 26100 8 23882 8 21637 7 22909 7 26157 8 12612 4
{85866A64-AEF6-143F-E053-6B04A8C06A15} CM16 7QQ 1000000 F N L 2019-03-15 00:00 E01021800 Epping Forest 003C 9468 3 20310 7 19901 7 5635 2 26159 8 10987 4
{85866A64-AEF7-143F-E053-6B04A8C06A15} CM1 6NF 225500 F N L 2019-02-25 00:00 E01021624 Chelmsford 004F 32035 10 32354 10 28950 9 24488 8 29662 10 23772 8
{85866A64-AEF8-143F-E053-6B04A8C06A15} CO15 4BT 320000 D N F 2019-02-22 00:00 E01022042 Tendring 014A 1299 1 965 1 1030 1 4672 2 1943 1 1378 1
{85866A64-AEF9-143F-E053-6B04A8C06A15} CO12 5FG 336000 D N F 2019-03-01 00:00 E01022002 Tendring 004C 16473 6 15310 5 12675 4 26099 8 11942 4 9408 3
{85866A64-AEFA-143F-E053-6B04A8C06A15} SS9 1LT 210000 F N L 2019-03-08 00:00 E01015830 Southend-on-Sea 012D 31888 10 30955 10 29338 9 15439 5 31062 10 30053 10
{85866A64-AA79-143F-E053-6B04A8C06A15} CB10 2BB 307500 S N F 2019-02-15 00:00 E01022079 Uttlesford 002D 25552 8 21904 7 23115 8 23823 8 29948 10 23044 8
{85866A64-AA7A-143F-E053-6B04A8C06A15} CM0 8TL 247500 S N F 2019-03-06 00:00 E01021878 Maldon 008C 24234 8 17330 6 20968 7 23454 8 22276 7 17718 6
{85866A64-AA7B-143F-E053-6B04A8C06A15} CM3 5TS 280000 S N F 2019-01-25 00:00 E01021611 Chelmsford 019A 29960 10 29487 9 27772 9 27886 9 31237 10 19667 6
{85866A64-AA7C-143F-E053-6B04A8C06A15} CM16 5DP 407000 S N F 2019-02-21 00:00 E01021763 Epping Forest 005D 23322 8 19500 6 20336 7 10121 4 27743 9 23132 8
{85866A64-AA7D-143F-E053-6B04A8C06A15} CO7 9HW 255000 S N F 2019-03-06 00:00 E01021737 Colchester 017B 22925 7 22881 7 22398 7 31210 10 18304 6 20513 7
{85866A64-AA7E-143F-E053-6B04A8C06A15} SS4 3BG 284000 S N F 2019-03-08 00:00 E01021928 Rochford 002C 24932 8 19758 7 22636 7 25751 8 28958 9 13218 5
{85866A64-AA7F-143F-E053-6B04A8C06A15} CO7 7YH 260000 S N F 2019-02-15 00:00 E01022052 Tendring 005D 19412 6 18796 6 13021 4 31188 10 11178 4 17452 6
{85866A64-AA80-143F-E053-6B04A8C06A15} IG10 3TU 410000 S N F 2019-02-22 00:00 E01021775 Epping Forest 011B 10606 4 12393 4 10697 4 1953 1 17673 6 7365 3
{85866A64-AA82-143F-E053-6B04A8C06A15} CO3 9XW 295000 D N F 2019-02-18 00:00 E01021690 Colchester 014B 24250 8 22116 7 20119 7 27771 9 19820 7 21705 7
{85866A64-AA83-143F-E053-6B04A8C06A15} CM15 0XE 445000 T N F 2019-03-14 00:00 E01021443 Brentwood 001C 14807 5 15059 5 23251 8 12093 4 29455 9 11836 4
{85866A64-AA84-143F-E053-6B04A8C06A15} CO15 4EU 182000 D N F 2019-03-01 00:00 E01022044 Tendring 013E 6985 3 7189 3 4225 2 12849 4 5916 2 4758 2
{85866A64-AA85-143F-E053-6B04A8C06A15} SS13 3HW 238500 T N F 2019-02-07 00:00 E01021317 Basildon 018E 2634 1 3656 2 3905 2 315 1 7082 3 1511 1
{85866A64-AA86-143F-E053-6B04A8C06A15} SS5 4UN 370000 S N F 2019-03-12 00:00 E01021941 Rochford 003I 31708 10 30948 10 30664 10 29011 9 32310 10 22070 7
{85866A64-AA87-143F-E053-6B04A8C06A15} SS15 5QE 353250 D N F 2019-03-15 00:00 E01021274 Basildon 010D 11620 4 10986 4 11700 4 7092 3 12932 4 7759 3
{85866A64-AA88-143F-E053-6B04A8C06A15} CM22 6LT 275000 S N F 2019-02-08 00:00 E01022062 Uttlesford 005A 31238 10 31540 10 31591 10 25528 8 32376 10 27406 9
{85866A64-AA89-143F-E053-6B04A8C06A15} CM1 6QR 146000 F N L 2019-02-26 00:00 E01021621 Chelmsford 007B 23198 8 23852 8 26567 9 15829 5 30199 10 15917 5
{85866A64-AA8A-143F-E053-6B04A8C06A15} CM15 9LX 268000 T N F 2019-01-25 00:00 E01021461 Brentwood 004D 12860 4 10654 4 12049 4 10625 4 17909 6 6332 2
{85866A64-AA8B-143F-E053-6B04A8C06A15} CM12 0UD 450000 D N F 2019-03-01 00:00 E01021252 Basildon 002E 30593 10 31139 10 31725 10 28838 9 24869 8 28950 9
{85866A64-AA8C-143F-E053-6B04A8C06A15} EN9 2LF 377500 T N F 2019-02-28 00:00 E01021794 Epping Forest 002D 17854 6 13753 5 22056 7 13446 5 23832 8 10957 4
{85866A64-AA8E-143F-E053-6B04A8C06A15} SS16 5EH 251500 T N F 2019-03-14 00:00 E01021296 Basildon 020C 12969 4 12018 4 11559 4 3827 2 19569 6 11857 4
{85866A64-AA8F-143F-E053-6B04A8C06A15} IG6 2QU 529000 T N F 2019-03-07 00:00 E01003700 Redbridge 006C 16080 5 14281 5 14975 5 12573 4 22969 7 14790 5
{85866A64-AA90-143F-E053-6B04A8C06A15} CM3 5UA 360000 D N F 2019-02-22 00:00 E01021611 Chelmsford 019A 29960 10 29487 9 27772 9 27886 9 31237 10 19667 6
{85866A64-AA91-143F-E053-6B04A8C06A15} SS13 3EN 250000 T N F 2019-03-01 00:00 E01021317 Basildon 018E 2634 1 3656 2 3905 2 315 1 7082 3 1511 1
{85866A64-AA92-143F-E053-6B04A8C06A15} SS15 5EP 290000 T N F 2019-02-20 00:00 E01021292 Basildon 017B 3402 2 3303 2 2862 1 379 1 7041 3 5827 2
{85866A64-AA93-143F-E053-6B04A8C06A15} IG10 3PY 365000 T N F 2019-03-06 00:00 E01021771 Epping Forest 013A 5221 2 4339 2 5344 2 7090 3 12147 4 3244 1
{85866A64-AA94-143F-E053-6B04A8C06A15} RM15 5QA 266000 T N F 2019-03-20 00:00 E01015927 Thurrock 007A 9921 4 7322 3 10173 4 6962 3 17341 6 6118 2
{85866A64-AA95-143F-E053-6B04A8C06A15} CM6 1HG 345000 F N F 2019-03-08 00:00 E01022067 Uttlesford 007B 21988 7 16739 6 18171 6 23066 8 21253 7 17256 6
{85866A64-AA96-143F-E053-6B04A8C06A15} SS13 2DY 295000 S N F 2019-03-25 00:00 E01021313 Basildon 018A 13200 5 19275 6 14851 5 4237 2 12298 4 9836 3
{85866A64-AA97-143F-E053-6B04A8C06A15} SS16 5EQ 260000 T N F 2019-02-06 00:00 E01021296 Basildon 020C 12969 4 12018 4 11559 4 3827 2 19569 6 11857 4
{85866A64-AA98-143F-E053-6B04A8C06A15} CB11 3LZ 1200000 D N F 2019-03-01 00:00 E01022099 Uttlesford 004D 29390 9 32097 10 32115 10 30826 10 32431 10 28089 9
{85866A64-AA9A-143F-E053-6B04A8C06A15} SS2 6SP 305000 S N F 2019-02-15 00:00 E01015868 Southend-on-Sea 002F 8729 3 7994 3 8780 3 10202 4 12151 4 3963 2
{85866A64-AA9B-143F-E053-6B04A8C06A15} CO6 1BB 288000 D N F 2019-03-14 00:00 E01021662 Colchester 010B 20670 7 22343 7 18360 6 29971 10 24396 8 21794 7
{85866A64-ACD3-143F-E053-6B04A8C06A15} SS6 8JE 315000 D N F 2019-03-22 00:00 E01021962 Rochford 008C 18963 6 14646 5 21075 7 14471 5 25930 8 11135 4
{85866A64-ACD4-143F-E053-6B04A8C06A15} CO13 0LZ 192500 S N F 2019-03-01 00:00 E01022010 Tendring 008F 15831 5 15039 5 10479 4 26205 8 13626 5 9694 3
{85866A64-ACD5-143F-E053-6B04A8C06A15} CO2 9NX 222000 T N F 2019-02-08 00:00 E01021714 Colchester 015D 17127 6 16855 6 15398 5 18497 6 14036 5 11772 4
{85866A64-ACD6-143F-E053-6B04A8C06A15} SS8 8EQ 250000 S N F 2019-02-28 00:00 E01021493 Castle Point 009B 16118 5 14734 5 15342 5 15641 5 20484 7 7486 3
{85866A64-ACD7-143F-E053-6B04A8C06A15} CO13 9AS 315000 F N L 2019-02-28 00:00 E01021986 Tendring 006B 17345 6 18319 6 12566 4 12083 4 12841 4 18560 6
{85866A64-ACD8-143F-E053-6B04A8C06A15} EN9 1BL 300000 T N F 2019-01-31 00:00 E01021818 Epping Forest 009C 13781 5 11077 4 11471 4 9618 3 20122 7 12714 4
{85866A64-ACD9-143F-E053-6B04A8C06A15} IG9 6BU 807000 S N F 2019-02-20 00:00 E01021742 Epping Forest 015A 19657 6 15116 5 16939 6 11962 4 24570 8 15892 5
{85866A64-ACDA-143F-E053-6B04A8C06A15} SS7 3UL 312500 S N F 2019-03-01 00:00 E01021510 Castle Point 002B 22348 7 20969 7 19176 6 30215 10 15534 5 14229 5
{85866A64-ACDB-143F-E053-6B04A8C06A15} SS4 1TX 135000 F N L 2019-03-04 00:00 E01021930 Rochford 007A 17611 6 16685 6 13250 5 23771 8 18878 6 12833 4
{85866A64-ACDC-143F-E053-6B04A8C06A15} CM3 5QE 380000 S N F 2019-02-22 00:00 E01021614 Chelmsford 019D 31259 10 27688 9 30142 10 25582 8 30841 10 18963 6
{85866A64-ACDD-143F-E053-6B04A8C06A15} CO6 1HN 195000 T N F 2019-02-21 00:00 E01021678 Colchester 010E 16239 5 21261 7 16025 5 6227 2 19012 6 17159 6
{85866A64-ACDE-143F-E053-6B04A8C06A15} CO15 5XU 200000 S N F 2019-03-22 00:00 E01022033 Tendring 012B 11303 4 9315 3 7462 3 26175 8 7716 3 7552 3
{85866A64-ACDF-143F-E053-6B04A8C06A15} SS8 9RD 566500 D N F 2019-02-26 00:00 E01021505 Castle Point 008D 22633 7 24378 8 20780 7 15769 5 20594 7 9299 3
{85866A64-ACE0-143F-E053-6B04A8C06A15} RM1 4DU 390000 S N F 2019-02-22 00:00 E01002334 Havering 008B 22951 7 20365 7 27498 9 12905 4 30122 10 16703 6
{8F1B26BD-4597-53DB-E053-6C04A8C03649} N22 7AQ 575000 F N L 2019-07-12 00:00 E01001962 Haringey 004A 18441 6 15087 5 20622 7 13125 4 24951 8 29142 9
{8F1B26BD-4598-53DB-E053-6C04A8C03649} E8 1FR 709000 F N L 2019-06-21 00:00 E01001762 Hackney 017D 9543 3 11630 4 12792 4 11315 4 10464 4 28052 9
{8F1B26BD-4599-53DB-E053-6C04A8C03649} E10 6JE 300000 F N L 2019-07-01 00:00 E01004367 Waltham Forest 019D 9423 3 11525 4 12972 4 3874 2 16699 6 13087 4
{8F1B26BD-459A-53DB-E053-6C04A8C03649} E10 6PB 410000 F N L 2019-06-27 00:00 E01004372 Waltham Forest 023C 22386 7 24738 8 30666 10 24263 8 24401 8 22697 7
{8F1B26BD-459B-53DB-E053-6C04A8C03649} EN2 8LA 1000000 F N L 2019-06-21 00:00 E01001412 Enfield 004C 8853 3 10956 4 13733 5 5796 2 26509 9 13225 5
{8F1B26BD-459D-53DB-E053-6C04A8C03649} E3 4NA 415000 F N L 2019-06-25 00:00 E01004267 Tower Hamlets 012D 6672 3 4497 2 9591 3 2294 1 14406 5 23029 8
{8F1B26BD-459E-53DB-E053-6C04A8C03649} E11 1PL 415000 F N L 2019-05-31 00:00 E01004468 Waltham Forest 015E 16258 5 18667 6 22110 7 10169 4 24560 8 22659 7
{8F1B26BD-459F-53DB-E053-6C04A8C03649} W4 5AG 1075000 F N L 2019-07-05 00:00 E01002692 Hounslow 029C 17842 6 16096 5 20186 7 16367 5 28761 9 26761 9
{8F1B26BD-45A1-53DB-E053-6C04A8C03649} E2 6AT 600000 F N L 2019-06-21 00:00 E01004313 Tower Hamlets 009B 11032 4 8127 3 15213 5 11763 4 15811 5 17146 6
{8F1B26BD-45A2-53DB-E053-6C04A8C03649} E8 1AB 470000 F N L 2019-07-17 00:00 E01001763 Hackney 020C 12683 4 12525 4 20885 7 11700 4 20253 7 22081 7
{8F1B26BD-45A3-53DB-E053-6C04A8C03649} E17 6LZ 400000 T N L 2019-07-05 00:00 E01004393 Waltham Forest 014B 12894 4 11571 4 20234 7 16076 5 14785 5 21131 7
{8F1B26BD-45A4-53DB-E053-6C04A8C03649} E17 5SB 240000 F N L 2019-06-21 00:00 E01004400 Waltham Forest 009C 8395 3 8027 3 10436 4 8140 3 17094 6 13196 5
{8F1B26BD-45A5-53DB-E053-6C04A8C03649} W4 2RZ 360000 F N L 2019-05-31 00:00 E01002572 Hounslow 007B 7879 3 5815 2 7673 3 12416 4 8490 3 15172 5
{8F1B26BD-45A6-53DB-E053-6C04A8C03649} N4 2GQ 640000 F N L 2019-06-28 00:00 E01033701 Hackney 002F 2578 1 1749 1 5333 2 4868 2 11339 4 8128 3
{8F1B26BD-45A7-53DB-E053-6C04A8C03649} N5 1TU 420000 F N L 2019-07-18 00:00 E01002752 Islington 009E 12440 4 8376 3 10958 4 10110 4 17498 6 23012 8
{8F1B26BD-45A8-53DB-E053-6C04A8C03649} N22 6RR 340295 F N L 2019-06-28 00:00 E01002027 Haringey 010A 8044 3 6864 3 8658 3 8530 3 13450 5 13056 4
{8F1B26BD-45A9-53DB-E053-6C04A8C03649} NW2 4ES 582500 F N L 2019-06-07 00:00 E01000560 Brent 023D 15170 5 16020 5 15775 5 9069 3 24133 8 25820 8
{8F1B26BD-45AA-53DB-E053-6C04A8C03649} E4 9RH 922000 T N F 2019-07-04 00:00 E01004386 Waltham Forest 003C 25868 8 27219 9 24801 8 17798 6 25645 8 22079 7
{8F1B26BD-45AB-53DB-E053-6C04A8C03649} UB9 6RD 660000 D N F 2019-03-06 00:00 E01002441 Hillingdon 003D 11576 4 10413 4 12737 4 5963 2 11847 4 11870 4
{8F1B26BD-45AC-53DB-E053-6C04A8C03649} EN4 9SE 322000 F N L 2019-07-05 00:00 E01000170 Barnet 006A 14031 5 10719 4 10414 4 8594 3 25024 8 21554 7
{8F1B26BD-45AD-53DB-E053-6C04A8C03649} N14 4JR 385000 F N L 2019-06-28 00:00 E01001525 Enfield 020E 17196 6 15368 5 19559 6 15865 5 23866 8 27045 9
{8F1B26BD-45AE-53DB-E053-6C04A8C03649} E1 8RB 395000 F N L 2019-06-14 00:00 E01000005 City of London 001E 8678 3 6029 2 7833 3 31059 10 17907 6 12370 4
{8F1B26BD-45AF-53DB-E053-6C04A8C03649} TW14 8PB 228000 F N L 2019-05-31 00:00 E01002557 Hounslow 023A 10494 4 11062 4 14669 5 10900 4 20088 7 8779 3
{8F1B26BD-45B0-53DB-E053-6C04A8C03649} W5 2PZ 696500 F N L 2019-05-29 00:00 E01001235 Ealing 020D 19722 7 19912 7 24417 8 7228 3 23433 8 24888 8
{8F1B26BD-45B1-53DB-E053-6C04A8C03649} NW9 0FG 470000 F N L 2019-04-01 00:00 E01000516 Brent 002C 14898 5 15553 5 19342 6 10621 4 23970 8 16778 6
{8F1B26BD-45B2-53DB-E053-6C04A8C03649} HA5 1SL 725000 D N F 2019-06-27 00:00 E01002437 Hillingdon 006E 29735 10 28358 9 30166 10 25370 8 30225 10 31182 10
{8F1B26BD-45B3-53DB-E053-6C04A8C03649} N6 4EY 800000 F N L 2019-04-26 00:00 E01002011 Haringey 030D 13214 5 13895 5 14301 5 4212 2 23360 8 24177 8
{8F1B26BD-45B4-53DB-E053-6C04A8C03649} E4 9XE 208000 F N L 2019-07-01 00:00 E01004419 Waltham Forest 008E 13460 5 18459 6 20853 7 9748 3 26058 8 17203 6
{9B361206-17BF-1904-E053-6B04A8C0EEB5} TQ6 0EX 540000 T N F 2019-12-06 00:00 E01020188 South Hams 004D 17489 6 23907 8 25303 8 29674 10 28820 9 28651 9
{9B361206-17C0-1904-E053-6B04A8C0EEB5} EX7 0LS 315000 D N F 2019-11-29 00:00 E01020231 Teignbridge 005C 18779 6 17483 6 15104 5 25394 8 16218 5 10636 4
{9B361206-17C1-1904-E053-6B04A8C0EEB5} EX37 9RL 600000 D N F 2019-12-02 00:00 E01020097 North Devon 013A 13309 5 20899 7 25133 8 31411 10 25704 8 16223 5
{9FF0D969-6881-11ED-E053-6C04A8C06383} TW7 6JT 383000 F N L 2019-10-11 00:00 E01002658 Hounslow 015D 24569 8 27868 9 30583 10 12728 4 22019 7 29033 9
{9B361206-17C3-1904-E053-6B04A8C0EEB5} EX12 2SS 210000 D N F 2019-11-12 00:00 E01019949 East Devon 010D 28437 9 23171 8 22391 7 31761 10 29255 9 19749 7
{9B361206-17C4-1904-E053-6B04A8C0EEB5} EX2 7NQ 329950 T Y F 2019-08-06 00:00 E01020033 Exeter 015C 17593 6 12417 4 11876 4 17951 6 20467 7 20861 7
{9B361206-17C5-1904-E053-6B04A8C0EEB5} PL20 7PD 225000 T N F 2019-12-16 00:00 E01020316 West Devon 007D 26608 9 28618 9 28989 9 32162 10 25969 8 31763 10
{9B361206-17C6-1904-E053-6B04A8C0EEB5} PL20 6JJ 200000 D N F 2019-11-29 00:00 E01020317 West Devon 006A 20801 7 24320 8 19297 6 30798 10 19355 6 22971 7
{9B361206-17C7-1904-E053-6B04A8C0EEB5} TQ3 2LJ 240000 D N F 2019-12-09 00:00 E01015203 Torbay 012B 21635 7 21899 7 15994 5 25167 8 14966 5 19150 6
{9B361206-17C8-1904-E053-6B04A8C0EEB5} EX4 6RP 126000 F N L 2019-11-20 00:00 E01020019 Exeter 004B 27981 9 31072 10 32691 10 27095 9 26357 9 28409 9
{9B361206-17C9-1904-E053-6B04A8C0EEB5} PL21 9FZ 232000 S Y F 2019-07-04 00:00 E01020167 South Hams 005G 32466 10 31343 10 26453 9 31703 10 27317 9 29951 10
{9B361206-17CA-1904-E053-6B04A8C0EEB5} EX12 2FH 199995 F Y L 2019-06-28 00:00 E01019947 East Devon 010B 15548 5 12568 4 12918 4 21100 7 16557 6 14891 5
{9B361206-17CB-1904-E053-6B04A8C0EEB5} PL9 8GJ 255000 S Y F 2019-10-21 00:00 E01020187 South Hams 009D 19157 6 17077 6 12165 4 26414 9 21098 7 23777 8
{9B361206-17CC-1904-E053-6B04A8C0EEB5} PL21 9FZ 260000 D Y F 2019-10-11 00:00 E01020167 South Hams 005G 32466 10 31343 10 26453 9 31703 10 27317 9 29951 10
{9B361206-17CD-1904-E053-6B04A8C0EEB5} EX15 1UZ 110000 F N L 2019-10-18 00:00 E01020055 Mid Devon 007B 13938 5 12617 4 10767 4 23905 8 17267 6 5738 2
{9B361206-17CE-1904-E053-6B04A8C0EEB5} EX22 6FQ 215000 T Y F 2019-10-18 00:00 E01020294 Torridge 008C 6946 3 6944 3 5974 2 26803 9 8991 3 4478 2
{9B361206-17CF-1904-E053-6B04A8C0EEB5} EX1 3UX 399995 D Y F 2019-09-23 00:00 E01019893 East Devon 006C 20288 7 19052 6 19451 6 30421 10 28017 9 17851 6
{9B361206-17D0-1904-E053-6B04A8C0EEB5} TQ9 5WY 470950 T Y F 2019-10-16 00:00 E01020184 South Hams 003E 14439 5 7265 3 8129 3 19941 7 20627 7 28951 9
{9B361206-17D1-1904-E053-6B04A8C0EEB5} EX31 3GD 289995 D Y F 2019-10-16 00:00 E01020086 North Devon 012C 26013 8 24789 8 22745 7 18979 6 19586 6 20117 7
{9B361206-17D3-1904-E053-6B04A8C0EEB5} EX31 3TW 453000 D Y F 2019-10-25 00:00 E01020106 North Devon 011B 19704 6 21352 7 17481 6 32722 10 12731 4 23766 8
{9B361206-17D4-1904-E053-6B04A8C0EEB5} EX17 1FE 229995 S Y F 2019-03-29 00:00 E01020042 Mid Devon 011B 28993 9 26173 8 25620 8 27619 9 28636 9 17461 6
{9FF0D969-6884-11ED-E053-6C04A8C06383} UB10 9NA 320000 F N L 2019-10-24 00:00 E01002513 Hillingdon 013D 21967 7 21007 7 16832 6 15420 5 22683 7 26677 9
{9FF0D969-6886-11ED-E053-6C04A8C06383} W13 8RD 399000 F N L 2019-11-15 00:00 E01001222 Ealing 019A 12874 4 13108 4 16185 5 13668 5 20841 7 25621 8
{9B361206-17D7-1904-E053-6B04A8C0EEB5} EX3 0FG 430000 S Y F 2019-10-25 00:00 E01020033 Exeter 015C 17593 6 12417 4 11876 4 17951 6 20467 7 20861 7
{9FF0D969-6888-11ED-E053-6C04A8C06383} HA2 6BU 275000 F N L 2019-04-05 00:00 E01002161 Harrow 021C 19247 6 11499 4 18084 6 21803 7 27275 9 29498 9
{9B361206-17D9-1904-E053-6B04A8C0EEB5} TQ9 6GU 360000 S Y F 2019-04-18 00:00 E01020181 South Hams 003B 17792 6 11145 4 10032 4 26856 9 15400 5 19881 7
{9B361206-17DA-1904-E053-6B04A8C0EEB5} TQ1 4NW 350000 F Y L 2019-10-31 00:00 E01015239 Torbay 002D 5045 2 3574 2 2769 1 14742 5 3736 2 13841 5
{9B361206-17DB-1904-E053-6B04A8C0EEB5} TQ9 7FN 365000 D Y F 2019-10-29 00:00 E01020188 South Hams 004D 17489 6 23907 8 25303 8 29674 10 28820 9 28651 9
{9B361206-17DC-1904-E053-6B04A8C0EEB5} TQ1 3LD 500000 F Y L 2019-10-31 00:00 E01015236 Torbay 002A 11715 4 14486 5 9116 3 5638 2 13778 5 16132 5
{9B361206-17DD-1904-E053-6B04A8C0EEB5} EX2 7NQ 354950 D Y F 2019-09-27 00:00 E01020033 Exeter 015C 17593 6 12417 4 11876 4 17951 6 20467 7 20861 7
{9FF0D969-6894-11ED-E053-6C04A8C06383} W3 7UN 220000 F N L 2019-07-02 00:00 E01001245 Ealing 024F 18507 6 16403 5 22559 7 13345 5 24008 8 26961 9
{9B361206-17DF-1904-E053-6B04A8C0EEB5} TQ7 2FA 195000 T N F 2019-10-03 00:00 E01020178 South Hams 011C 19888 7 24483 8 23902 8 32446 10 27927 9 24866 8
{9B361206-17E1-1904-E053-6B04A8C0EEB5} TQ9 6GR 375000 S Y F 2019-10-31 00:00 E01020181 South Hams 003B 17792 6 11145 4 10032 4 26856 9 15400 5 19881 7
{9B361206-17E2-1904-E053-6B04A8C0EEB5} EX31 3UG 330000 D Y F 2019-10-18 00:00 E01020105 North Devon 011A 22666 7 19686 6 17996 6 29187 9 15816 5 14395 5
{9B361206-275C-1904-E053-6B04A8C0EEB5} N8 0SG 285000 F N L 2019-11-22 00:00 E01002003 Haringey 023C 5261 2 6416 2 9560 3 2282 1 12153 4 8164 3
{9B361206-275D-1904-E053-6B04A8C0EEB5} E1W 2JF 630000 F N L 2019-09-06 00:00 E01004292 Tower Hamlets 026A 27385 9 27476 9 31829 10 16173 5 23286 8 29342 9
{9B361206-275E-1904-E053-6B04A8C0EEB5} E11 2JU 1185000 S N F 2019-09-24 00:00 E01003796 Redbridge 018D 25148 8 25034 8 28543 9 10846 4 31375 10 30118 10
{9B361206-275F-1904-E053-6B04A8C0EEB5} E17 4PL 575000 T N F 2019-11-29 00:00 E01004347 Waltham Forest 011C 18452 6 16302 5 22673 7 13403 5 25222 8 23465 8
{9B361206-2760-1904-E053-6B04A8C0EEB5} N9 7PZ 196000 F N L 2019-12-04 00:00 E01001480 Enfield 024D 7739 3 7133 3 10874 4 11738 4 15576 5 13809 5

Checking for missing values

Before purely exploring the data and visualize what’s inside of it, we need to check for missing values in the data. I decided to do this check only after the joining of the tables because deleting values before wouldn’t have brought anything.

Missing values:
missing %
Unique_transaction_code 0 0
Postcode 0 0
Price 0 0
Housing_type 0 0
New_or_old 0 0
Freehold_Leasehold 0 0
Date 0 0
LSOA_code 0 0
LSOA_name 0 0
IMD_rank 0 0
IMD_decile 0 0
Income_rank 0 0
Income_decile 0 0
Employment_rank 0 0
Employment_decile 0 0
Crime_rank 0 0
Crime_decile 0 0
Health_Deprivation_rank 0 0
Health_Deprivation_decile 0 0
Skill_rank 0 0
Skill_decile 0 0

As we can see and as I was expecting, there are no missing values inside of the dataset, this is mainly due to the fact that during matching we had to have a valid LSOA code in England that would make us able to join the IMD information, this asked to filter for code starting by “E” and therefore eliminating NAs.

References

Price paid data : https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#yearly-file Contains HM Land Registry data © Crown copyright and database right 2020. This data is licensed under the Open Government Licence v3.0.

IMD data (table2) : https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 Contains public sector information licensed under the Open Government Licence v3.0.

Postcodes Data: https://www.doogal.co.uk/UKPostcodes.php Contains OS data © Crown copyright and database rights 2020 Contains Royal Mail data © Royal Mail copyright and database rights 2020 Contains National Statistics data © Crown copyright and database rights 2020 Contains data from Wikipedia covered by the Creative Commons license

Exploratory data analysis

In this section we will import the cleaned data and use it to start exploring the dataset and visualizing the information inside of it.

Exploration of the table

We are now going to look a bit more at the prices in details to see how they are in the data, the mean and max values and other statistical values that can be interesting to explore and usefull to to answer the research questions:

Prices statistical values

In this section we will explore the different statistical values of the price such as the mean, the max, the min, the median and the quantiles:

To do so, we are going to summarise the table around the price and creating a whole new one containing only the newly calculated values.
Min Price 1st quantile Median Mean 3rd Quantile Max
1 152000 238000 353511 365000 411500000


Now let’s have a look at the density plot of the prices:
As we can see drawing the plot without limits does not help so we are going to draw it with limit on the x axis set to 10’000 and 3 millions. I have set those numbers in this way because in big cities you can find small housing for 20’000 therefore in smaller ones it might be possible to find some around 10’000. The 3 millions was set this way because you still find plenty of housing in London or other large cities around 1 to 2 millions but when you reach higher numbers those often comes with some more luxurious feature and are therefore “Prestige” housing which are on an other class.


This brings quite a lot of information for the rest of the project, in fact we have some extreme values in the dataset and those could well influence a lot our results and make the analysis way harder. In fact, the price of 411’500’000 is really high and therefore might change the results, it is needed to take this into account and extremes might need to be either aligned on a certain level or simply removed to make the analysis readable. The other strange and interesting point is the value of 1, it seems quite odd to have some real estate property costing only 1 pound, we do have to check how many small values are in the dataset to take them into account too.

We can see that we have 4296 housings over 3 millions which represent 0.46% of the complete table and 4097 housings under 10 thousands which represent 0.439% of the complete table.

In order to do a good analysis of the overall market we could maybe remove those values to do the regressions since those do not really reflect the “normal” market but extreme ones such as the most luxurious one which is cannot be compared from places to places since it’s mostly unique.

Before looking further let’s just have a look at the distribution of the sales of housing over 3 millions and under 10’000:

As we can see, the housing priced over 3 millions have the same type of distribution as when we take the overall dataset, there is nothing to really take out of this but it was important to check for pattern into this data too

While for the ones prices under 10’000 we can see that many are around 1000, to be honest I don’t really see what kind of property could be sold at that price, maybe those are rural parking spots but it is quite hard to know. the distribution is quite even and does not follow a precise rule.

Differences between new and old constructions

We will now check if there is a difference inside of the dataset which could help to explain some things later in between new and old buildings.
First, let’s have a look at the prices and their density:

New or old Mean Price Median Price
N 354741 230000
Y 345390 285000

To better see the difference let’s look at both by comparing them via boxplots:

This is quite interesting too, we can see that in general by looking at the median prices, the newly builded housing cost more than the old one but it seems that the extremes are more often present in the old buildings. This could be explained by the fact that a lot of “Prestige” Real estate is old and therefore the mean is further from the median compared to the newly built housing. An other point is that London is an old city and one of the costliest in the world it is therefore logic that a lot of old building sales could occur there and making this trend.



Before continuing on the prices, let’s just have a look at the distribution of the new and old housings on the market:
New or old Quantity Proportion
N 811269 0.869
Y 122834 0.131

As we can see, there are way more old houses sold on the market than new ones which is quite logic since a lot of the territory is already built and therefore it would be odd to find more new buildings than old with such a space limit.

To check the prices a bit more let’s have a look at the details, we have 229 newly built housing that sold for over 3 millions which represent 5.331% of all the housings over that price so we therefore have 4067 old housings sold over 3 millions representing the other 94.669%.

As we can see, the assumption made right before is confirmed as 94.669% of the buildings costing more than 3 millions are not newly built ones. let’s now check where those old buildings are:
LSOA name Amount
Kensington and Chelsea 015C 81
City of London 001F 41
Westminster 018D 28
Westminster 016D 26
Westminster 013E 23
Westminster 019E 20
Westminster 018C 19
Westminster 019A 18
Westminster 019C 17
County Durham 063B 15
Westminster 011E 15
Bristol 032B 14
Westminster 018B 14
Kensington and Chelsea 018E 13
Westminster 019F 13
Camden 028B 12
Kensington and Chelsea 011A 12
Kensington and Chelsea 012D 12
Westminster 018A 12
Westminster 019D 12
Ealing 006A 11
Hackney 027G 11
Westminster 020A 11
Barnet 033B 10
Camden 021B 10
Kensington and Chelsea 007E 10
Kensington and Chelsea 018A 10
Westminster 011B 10
Westminster 011C 10
Camden 002E 9
City of London 001G 9
Kensington and Chelsea 008B 9
Kensington and Chelsea 012A 9
Kensington and Chelsea 012B 9
Kensington and Chelsea 012C 9
Kensington and Chelsea 018B 9
Kensington and Chelsea 019C 9
West Berkshire 011F 9
Westminster 011A 9
Westminster 013D 9
Camden 017D 8
Islington 022D 8
Islington 022H 8
Kensington and Chelsea 007B 8
Kensington and Chelsea 012E 8
Kensington and Chelsea 020B 8
Leeds 111A 8
Richmond upon Thames 003B 8
Southwark 018B 8
Westminster 013B 8
Westminster 014A 8
Westminster 014B 8
Westminster 015A 8
Wigan 006A 8
Birmingham 138A 7
Bristol 054C 7
Camden 027B 7
Eastleigh 013D 7
Kensington and Chelsea 008A 7
Kensington and Chelsea 009D 7
Kensington and Chelsea 010C 7
Kingston upon Thames 009C 7
Manchester 054C 7
Merton 002A 7
Runnymede 005D 7
Sefton 004G 7
Southampton 023B 7
Sutton 012D 7
Birmingham 033F 6
Camden 018C 6
Camden 026B 6
Camden 028C 6
Elmbridge 018B 6
Hammersmith and Fulham 010D 6
Kensington and Chelsea 011E 6
Kensington and Chelsea 013A 6
Kensington and Chelsea 014D 6
Kensington and Chelsea 017E 6
Luton 021C 6
Manchester 055B 6
Milton Keynes 014F 6
Tower Hamlets 018D 6
Trafford 002E 6
Westminster 001D 6
Westminster 003C 6
Westminster 013F 6
Wiltshire 042D 6
Windsor and Maidenhead 018A 6
Woking 008A 6
Camden 002A 5
Camden 004A 5
Camden 021A 5
Crawley 001C 5
Croydon 028D 5
Elmbridge 016A 5
Elmbridge 018D 5
Hammersmith and Fulham 020E 5
Haringey 034C 5
Havant 005D 5
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St Edmundsbury 009C 1
St Edmundsbury 014E 1
St. Helens 004B 1
St. Helens 010B 1
St. Helens 014C 1
St. Helens 015D 1
St. Helens 019G 1
St. Helens 019J 1
Stafford 006C 1
Stafford 010B 1
Stafford 012B 1
Stevenage 002B 1
Stevenage 011D 1
Stockport 007C 1
Stockport 007D 1
Stockport 010E 1
Stockport 011B 1
Stockport 020D 1
Stockport 022A 1
Stockport 031D 1
Stockport 035D 1
Stockton-on-Tees 002E 1
Stockton-on-Tees 014G 1
Stoke-on-Trent 002D 1
Stoke-on-Trent 023D 1
Stoke-on-Trent 026D 1
Stoke-on-Trent 033A 1
Stratford-on-Avon 004E 1
Stratford-on-Avon 010E 1
Stratford-on-Avon 014A 1
Stroud 002A 1
Suffolk Coastal 007I 1
Suffolk Coastal 008B 1
Suffolk Coastal 011A 1
Suffolk Coastal 015A 1
Sunderland 007A 1
Sunderland 020F 1
Sunderland 026E 1
Sunderland 029C 1
Surrey Heath 001D 1
Surrey Heath 001E 1
Surrey Heath 001F 1
Surrey Heath 002B 1
Surrey Heath 002C 1
Surrey Heath 005E 1
Surrey Heath 008D 1
Surrey Heath 010A 1
Surrey Heath 012D 1
Sutton 011A 1
Sutton 018C 1
Sutton 019C 1
Sutton 022B 1
Sutton 024C 1
Swale 010E 1
Swale 013C 1
Swindon 005B 1
Swindon 008B 1
Swindon 009B 1
Swindon 012B 1
Swindon 012F 1
Swindon 014A 1
Swindon 014D 1
Swindon 017A 1
Swindon 023F 1
Swindon 028B 1
Tameside 002A 1
Tameside 013B 1
Tameside 016A 1
Tameside 020A 1
Tameside 030D 1
Tamworth 002A 1
Tandridge 002D 1
Tandridge 004D 1
Tandridge 012C 1
Taunton Deane 007D 1
Taunton Deane 009D 1
Taunton Deane 010B 1
Taunton Deane 011E 1
Taunton Deane 012A 1
Taunton Deane 014B 1
Teignbridge 001D 1
Teignbridge 002B 1
Teignbridge 003C 1
Teignbridge 010B 1
Telford and Wrekin 009A 1
Telford and Wrekin 023A 1
Tendring 008G 1
Tendring 013A 1
Tendring 014E 1
Tendring 018E 1
Test Valley 002A 1
Test Valley 003B 1
Test Valley 004E 1
Tewkesbury 001D 1
Tewkesbury 003B 1
Tewkesbury 003D 1
Tewkesbury 004A 1
Tewkesbury 006B 1
Thanet 002D 1
Thanet 008D 1
Thanet 011B 1
Thanet 011C 1
Thanet 012B 1
Three Rivers 011B 1
Three Rivers 011C 1
Three Rivers 012D 1
Thurrock 005A 1
Thurrock 013B 1
Thurrock 018A 1
Tonbridge and Malling 002G 1
Tonbridge and Malling 005E 1
Tonbridge and Malling 006B 1
Tonbridge and Malling 010A 1
Tonbridge and Malling 012D 1
Torbay 002A 1
Torbay 005C 1
Torbay 014A 1
Tower Hamlets 003C 1
Tower Hamlets 005C 1
Tower Hamlets 010F 1
Tower Hamlets 014A 1
Tower Hamlets 016A 1
Tower Hamlets 017B 1
Tower Hamlets 018A 1
Tower Hamlets 022D 1
Tower Hamlets 023C 1
Tower Hamlets 024A 1
Tower Hamlets 026B 1
Tower Hamlets 027A 1
Tower Hamlets 028A 1
Tower Hamlets 028I 1
Tower Hamlets 033B 1
Trafford 011D 1
Trafford 019A 1
Trafford 019B 1
Trafford 024C 1
Trafford 027B 1
Trafford 027C 1
Trafford 028A 1
Trafford 028C 1
Tunbridge Wells 003B 1
Tunbridge Wells 004B 1
Tunbridge Wells 004E 1
Tunbridge Wells 005D 1
Tunbridge Wells 006B 1
Tunbridge Wells 008B 1
Tunbridge Wells 008C 1
Tunbridge Wells 012D 1
Uttlesford 005A 1
Uttlesford 006B 1
Vale of White Horse 006H 1
Vale of White Horse 010C 1
Vale of White Horse 010D 1
Vale of White Horse 010E 1
Vale of White Horse 014B 1
Vale of White Horse 015B 1
Vale of White Horse 015D 1
Vale of White Horse 016E 1
Wakefield 005B 1
Wakefield 009D 1
Wakefield 012C 1
Wakefield 014A 1
Wakefield 015B 1
Wakefield 024A 1
Wakefield 028A 1
Wakefield 031D 1
Wakefield 038D 1
Walsall 027A 1
Walsall 038A 1
Waltham Forest 002B 1
Waltham Forest 004C 1
Waltham Forest 013D 1
Waltham Forest 014E 1
Waltham Forest 024C 1
Wandsworth 002B 1
Wandsworth 006D 1
Wandsworth 007D 1
Wandsworth 008E 1
Wandsworth 010A 1
Wandsworth 010B 1
Wandsworth 011E 1
Wandsworth 012A 1
Wandsworth 016A 1
Wandsworth 016D 1
Wandsworth 017A 1
Wandsworth 019A 1
Wandsworth 020B 1
Wandsworth 021B 1
Wandsworth 021E 1
Wandsworth 022D 1
Wandsworth 023D 1
Wandsworth 024D 1
Wandsworth 025A 1
Wandsworth 027A 1
Wandsworth 027E 1
Wandsworth 031D 1
Wandsworth 033D 1
Wandsworth 035A 1
Wandsworth 037C 1
Warrington 003C 1
Warrington 004C 1
Warrington 006E 1
Warrington 009B 1
Warrington 009C 1
Warrington 013B 1
Warrington 016A 1
Warrington 021C 1
Warrington 023C 1
Warrington 023E 1
Warrington 024A 1
Warwick 005B 1
Warwick 005G 1
Warwick 011H 1
Warwick 012A 1
Warwick 012E 1
Watford 005A 1
Watford 006D 1
Watford 009A 1
Watford 009D 1
Watford 012B 1
Waverley 001B 1
Waverley 004D 1
Waverley 007D 1
Waverley 011B 1
Waverley 012A 1
Waverley 012D 1
Waverley 015B 1
Waverley 015D 1
Waverley 015E 1
Waverley 018C 1
Waverley 018D 1
Wealden 001B 1
Wealden 014E 1
Wealden 016D 1
Wealden 018D 1
Wellingborough 003D 1
Wellingborough 005A 1
Wellingborough 007A 1
Welwyn Hatfield 001C 1
Welwyn Hatfield 007A 1
Welwyn Hatfield 015B 1
Welwyn Hatfield 016A 1
West Berkshire 001A 1
West Berkshire 002C 1
West Berkshire 005B 1
West Berkshire 006A 1
West Berkshire 009D 1
West Berkshire 011B 1
West Berkshire 013A 1
West Berkshire 019B 1
West Dorset 001B 1
West Dorset 004E 1
West Lancashire 004D 1
West Lancashire 011A 1
West Oxfordshire 002A 1
West Oxfordshire 003C 1
West Oxfordshire 010E 1
West Oxfordshire 011C 1
West Oxfordshire 012C 1
Westminster 003A 1
Westminster 003B 1
Westminster 003E 1
Westminster 005C 1
Westminster 006C 1
Westminster 007E 1
Westminster 007F 1
Westminster 008B 1
Westminster 009F 1
Westminster 009H 1
Westminster 009I 1
Westminster 011D 1
Westminster 012C 1
Westminster 014F 1
Westminster 015C 1
Westminster 015E 1
Westminster 016C 1
Westminster 017A 1
Westminster 020B 1
Westminster 021E 1
Westminster 023D 1
Westminster 023E 1
Weymouth and Portland 002B 1
Weymouth and Portland 003D 1
Wigan 013E 1
Wigan 017C 1
Wigan 019E 1
Wigan 021E 1
Wigan 024D 1
Wigan 026D 1
Wigan 027B 1
Wigan 031E 1
Wigan 036E 1
Wigan 038C 1
Wiltshire 007E 1
Wiltshire 007G 1
Wiltshire 011D 1
Wiltshire 012A 1
Wiltshire 012B 1
Wiltshire 019A 1
Wiltshire 022B 1
Wiltshire 022D 1
Wiltshire 023A 1
Wiltshire 023C 1
Wiltshire 024D 1
Wiltshire 025C 1
Wiltshire 026B 1
Wiltshire 048D 1
Wiltshire 050A 1
Wiltshire 058C 1
Wiltshire 062A 1
Winchester 004A 1
Winchester 004E 1
Winchester 005C 1
Winchester 006A 1
Winchester 006B 1
Winchester 006D 1
Winchester 009C 1
Winchester 011B 1
Winchester 012F 1
Winchester 013A 1
Winchester 013D 1
Winchester 014D 1
Windsor and Maidenhead 002B 1
Windsor and Maidenhead 002G 1
Windsor and Maidenhead 004E 1
Windsor and Maidenhead 005B 1
Windsor and Maidenhead 005D 1
Windsor and Maidenhead 009E 1
Windsor and Maidenhead 010A 1
Windsor and Maidenhead 011A 1
Windsor and Maidenhead 015A 1
Windsor and Maidenhead 017B 1
Windsor and Maidenhead 017D 1
Wirral 016A 1
Wirral 018D 1
Woking 003A 1
Woking 004E 1
Woking 006B 1
Woking 006C 1
Wokingham 001A 1
Wokingham 002E 1
Wokingham 006B 1
Wokingham 009C 1
Wokingham 011D 1
Wokingham 012H 1
Wokingham 013C 1
Wokingham 017B 1
Wolverhampton 001B 1
Wolverhampton 009B 1
Wolverhampton 012C 1
Wolverhampton 012D 1
Wolverhampton 018E 1
Wolverhampton 027A 1
Wolverhampton 032B 1
Wolverhampton 034A 1
Worcester 003A 1
Worcester 007C 1
Worcester 011E 1
Worcester 012A 1
Worthing 009A 1
Wychavon 007B 1
Wycombe 002A 1
Wycombe 003D 1
Wycombe 006B 1
Wycombe 015E 1
Wycombe 020B 1
Wycombe 023A 1
Wycombe 023B 1
Wycombe 024C 1
Wyre 007C 1
Wyre Forest 012E 1
York 003B 1
York 007B 1
York 011B 1
York 013C 1
York 013G 1

By browsing through the table we can see that a lost of the buildings are coming from the greater region of London (Westminster, chelsea, city of London, etc) but not only and some other regions still have a few housing that were sold for over 3 millions.

Difference in between housing types

We are now going to have a look at the different types of housing that we can find in the dataset, as like as for the new and old variable this could help to explain some of the results of the research questions.
Housing type Mean price Median price
D 420082 345000
F 304942 212500
O 1213602 268000
S 259309 215000
T 243095 183000

This is maybe one of the most interesting thing as of right now, we can see there that Semi-detached (S), Flats (F), and Terraced(T) housing follow the same logic and seem to be quite well distributed since the mean is more or less close to the median. Detached (D) housings are following the same logic but have, as it could be expected more pricey.

The key point here is the Other category which shows where it seems that we have most of the extremes, this could well be explained by the fact that this category might have the big apartments buildings inside of it which would explain the max value seem earlier of 411’500’000.

Just for the number, let’s check how many observations have a price over 3 millions for the other category and it’s proportion:

Unsurprisingly this category alone has 3050 representing 0.71% of the whole number of buildings over 3 millions which confirm in a way what was told before, there is sadly no data to be 100% sure about the fact that those are apartment buildings but the odds are high that this is the case.

Now, let’s check the distribution of the housign types in the new and old categries:

As we can see, the market is a bit different in what we build compare to what we have built until now, development of big cities push to mainly develop flats in them while people who are running away from those same cities want to have privacy and therefore ask for detached housing which leads to lower construction of semi-detached and Terraced housings.

Leasehold and freehold

In the dataset we have information about the way housing were bought, freehold meaning that the buyers owns the land and leasehold meaning that the buyer have the right from the landlord to live on his plot for a certain amount of time. Here his the table with the density plot and the distribution of them:

Freehold or Leasehold Mean Price Median Price
F 361321 250000
L 328158 193000
And their distribution in between the two categories:
Freehold or Leasehold Quantity Proportion
F 714124 0.765
L 219979 0.235

As we can clearly see, around three quarters of the properties are bought as freehold, and the last quarter as leasehold. Quite logically, in general freehold properties cost more than leasehold since the plot comes with the building itself. But what’s their distribution in terms on new and old?

As we can see, the proportion of leasehold is higher in the newly built housings, this may be due to the fact that the prices have increased since the 2008 crisis and to be able to sell more easily the buildings, the leasehold help decreasing the price and therefore reduce the price a little bit. And what about the types of housings?

Here we have the complete answer, most of the leasehold happens in the flats, meaning that the landlords decided to let someone build an apartment building on their plot while still keeping it, it is not uncommon since most of the old flats are the same. The key point is that the fact that the proportion of newly built homes as leasehold is higher that for old ones comes from what we are building today.

Even thought as we have seen earlier we build more detached houses than flats, the proportion is nearly the same while in the old building market the proportion of detached is quite higher, therefore this leads to a larger share of leasehold in the newly built market.

Evolution of the mean and median prices during the year

Just by curiosity, one interesting thing to explore is the evolution of the mean and median prices during the year which can be seen in the graph here:

As we can see, at least for monthly data, it does not seem to have any real trend on the evolution of prices during the year now let’s look at the evolution of the number of sales:
This time we have a bit more information, it seems that more sales, not a lot more but stil, are occurring in the second part of the year compared to the first part this could be explained by the fact that many people are either hard working around the beginning of the year to make the company statements or recovering from the winter holidays and therefore less interested into buying new houses.

But is this the same for both old and new housings, let’s find out:

As we can see here, it does not seem to have the same logic when looking at newly built buildings, those are quite better distributed compared to the old ones that still seems to be bought a bit more often at the end of the year. This may therefore be caused by fiscality purposes more than for delivering the housing to a client.

In the new buildings we just have 2 small peaks that are in june and december, the june one might be due to the fact that the good weather accelerate the delivery and the december one is certainly due to the will to deliver for the new year.

IMD decile and number of sales

One interesting thing to see would be the number of sales for each decile, we could assume instinctively that the less deprived regions are buying more than the most deprived since they should in some sort of way be “richer” let’s have a look at this:
IMD decile Quantity
1 66573
2 76508
3 86452
4 93002
5 98857
6 104031
7 103297
8 105076
9 102228
10 98079

As we can see, the most deprived (on the left) are effectively the regions with the least amount of building bought, but what is interesting is that the least deprived places (on the right) are not the regions where most of the exchanges are made, we will see later in the analysis how the prices evolves but this could be one of the factor that is influencing in this way.

This result is quite unsurprising but at least gives good information about how the market is working.

Analysis

In this part we are going to start looking at the datas in order to answer the reasearch question and give the answer at the end.

1st question : “Are the housing prices linked to the the Employment, Criminality, Income and Health deprivation levels of the different geographical regions ?”

Here the objective is to have a look at different component from de deprivation index and their effect on the prices to see which ones have the most and the least effect.

Correlation matrix

#>                         Income_rank Employment_rank Crime_rank Health_Deprivation_rank
#> Income_rank                   1.000           0.941      0.627                   0.792
#> Employment_rank               0.941           1.000      0.572                   0.836
#> Crime_rank                    0.627           0.572      1.000                   0.588
#> Health_Deprivation_rank       0.792           0.836      0.588                   1.000

As we can see Employment and income are highly correlated, we will therefore only look at one of them in the following analysis. This analysis will be executed with the deciles and not the rank for a simple reason, that will reduce the x axis nd make the picture easier to look at.

Regression and coefficients

Before even showing any regression line, I have to say that I am only going to prin the line itself and will avoid bringing all the 900’000 points on each graphics to lower the calculation time and in any way, so many points would only make the graphic having a dark theme more than bringing any important information.

Income

We will first look at the Income , its regression and correlation information:

As we can see, at first sight we have a relationship which is positive, meaning that when the revenue rank or score increase, the Price of the housing increase too. it is a logical relatonship in the way that people have more money and therefore are ready to pay more to have a better home.

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Final_analysis_table$Price and Final_analysis_table$Income_decile
#> t = 48, df = 934101, p-value <2e-16
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.0472 0.0513
#> sample estimates:
#>    cor 
#> 0.0492

As we can see, the correlation is really low, explaining only 4% of the evolution but the p-value is really low meaning that it is still statistically significant. It is therefore not to throw but it could clearly not be used all alone to explain anything.

With this model we have a intercept of 174839 and a slope of 30596 by decile which is quite a lot when you think about it. Even though the intercept is still quite higher than the minimum price we ca find in the most deprived area by looking at the tables for the 2nd question.

Crime

Now let’s have a look at the same things but for the crime decile:

We have again a positive relationship but this time it seems to be flatter than before. It does have a link but it looks like this time it is even lower than what it was for the income. lets find out by looking at the correlation informations:

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Final_analysis_table$Price and Final_analysis_table$Crime_decile
#> t = 21, df = 934101, p-value <2e-16
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.0198 0.0239
#> sample estimates:
#>    cor 
#> 0.0218

Once again the correlation is low, this time even lower being around 2%, but again the p-value is extremely low therefore this is still statistically significant but could hardly be used alone to explain anything. Still lets check the slope and itnercept of this:

With this model we have a intercept of 279841 and a slope of 12980 by decile which is not that much this time, it looks like the relationship is more complex. In fact crimes are happening a lot in wealthy areas too and therefore the crime ranking will be quite different from the Income one. This could be explored further by maybe looking at which crime type has a link and which has not in further research.

Health

Let’s now check for the health levels :

We do have again a positive relationship which seems, visually at least to be quite similar to the one we had with the income. Let’s look once again at the correlation values:

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Final_analysis_table$Price and Final_analysis_table$Health_Deprivation_decile
#> t = 69, df = 934101, p-value <2e-16
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.0695 0.0736
#> sample estimates:
#>    cor 
#> 0.0716

This time the correlation is a bit higher, around 7% and the p-value still indicate a that this is statistically significant which is quite interesting.

With this model we have a intercept of 105956 and a slope of 43209. This time it is closer to the values we have in the reality in terms of minimas and maximas even though it is not really perfect either.

To conclude for this question, there is an effect for each of the ranking we looked about sadly the effect is not that big, the key point to take here is the fact that at least when we look at this dataset the p-value is extremely low, therefore even though we are not explaining a lot of the variation with each one of the variables those could be included inside a model with the volume and other classical aspect from housing to create some more precise models. But this should still be analyzed by potential further research.

2nd question : “Are the mean, median prices and overall price range in an area linked to the IMD?”

We can already see a trend here, the boxplot are slowly going to the right but let’s have a look at each individual value in a table to really see those numbers :
IMD decile Minimum price 1st quantile Median price mean price 3rd quantile Maximum price
1 100 75000 113000 166090 162500 56700000
2 100 104000 152000 252012 239950 95000000
3 1 122500 182000 318627 295000 276570000
4 100 140000 206000 319421 315000 145000000
5 100 159950 232000 345599 345000 315000000
6 100 172000 250000 371628 374000 262890000
7 100 181500 262995 399046 382500 337000000
8 1 192000 275000 401738 400000 139480000
9 100 210000 296000 407304 430000 103050000
10 1 250000 350000 456046 502500 411500000

Let’s just plot that and then we will speak about it, we are not going to put the min and the max in the plt since this is not bringing that much to the analysis.

As it is nicely shown in the graph, the less deprived an area is in general, the higher the price is, it is quite logic but I would have though that some values could have been way closer than what they are because even if people are quite poor you can find some wealthy investment in the same area but this is not even really shown in this result. We can see this logic a little bit by looking at the mean and the 3rd quantile, they cross each other and the 3rd quantile is higher than the mean only for the 3 last deciles meaning that in the other deciles, the extremes have more “power” and move the mean higher than in the less deprived areas.

If we really look at the prices in the table, we can see that in all columns in between the least an and the most deprived we have a factor of 3 to 5 times the price going for example from 160 thousands up to 500 thousands for the 3rd quantile which is really impressive. What is more, we can see that the 1st quantile of the least deprived ares is at 250 thousands while as I just said, the 3rd quantile of the most deprived area is at 160 thousands, meaning that most of the 25 cheapest percent of the least deprived area housings cost more than the 25% most expensive ones in the highly deprived areas.

To conclude for this question,I believe that we can really see a link between the IMD and the price of the housing in a region. It is, in the end something that is logic but I am quite surprised by the level of this link.

3rd question : “Is there a difference in the type of housings depending on the deprivation levels?”

In order to answer this question we need to visualize the type of housing sold by LSOA:

As we can clearly see d have differences in what is sold in the different IMD levels. It seems that most of the sales from the least deprived area are detached and semi-detached housings while in the most deprived ones we have a lot of flats and terraced housings.

This is interesting in the way that it explains part of the price difference because as we saw in the EDA part, the price of the detached housing is generally higher than the price of the other housings. It does not explain the full difference but at least help to understand it a bit better. Let’s look at the distribution of prices by type of housing in the most and least deprived areas to have a really clear view:

IMD decile Housing tpe 1st quantile Median price mean price 3rd quantile
1 D 160000 199950 220842 250000
1 F 72000 105795 157357 165000
1 O 70000 150000 573441 375000
1 S 99500 130000 139625 165995
1 T 65000 90000 107731 135000
10 D 329950 438000 536146 615000
10 F 170000 240000 308310 335000
10 O 150000 375000 1263185 820000
10 S 232500 315000 362471 425000
10 T 214000 287500 348976 395000

Although at first we could have though that the price link seen at the question 2 was due to the different type of housing we just looked at, it seems that by looking at this table, that part of this difference is coming more from the other types of housings where the prices have way bigger differences than the detached houses ones.

To conclude this question. We do have different “housing consumption” depending on the level of deprivation from the people which was quite logic in some way but it helps understanding more about the market.

4th question : “Are we building more in the less deprived area or in the most deprived?”

To answer this question we need to visualize the number of new building sold by IMD decile:

IMD decile New or old Count
1 Y 7752
2 Y 9662
3 Y 11547
4 Y 10308
5 Y 13139
6 Y 13746
7 Y 14065
8 Y 14875
9 Y 13348
10 Y 14392

Here we can clearly see that the majority of newly built housings are done in “mediumly” to lightly deprived areas since decile 5 to 10 have nearly all 2 times more newly built housing that were sold compared to the decile 1 which is the most deprived where there is quite a lot less new developments or at least it seems to be the case in the number of sales. This is logic in the way that those regions generally have way less money available but we could have thought that many developers would try to profit from those areas to transform them into better places and modify the city to make it look cleaner. This does not seem to happen and developers are or at least seemed to have played it quite safely by developing mainly into less deprived area that are looking better in the eyes of the buyers.

Conclusion

This project was a great chance offered by the professor to work for the first time of my life on such a scale. It was quite challenging but very rewarding in terms of knowledge and understanding. It made me realize hat working with large dataset is really hard and even when they are properly cleaned we need to be aware that each thing we want to do may take quite some times, especially in my case with my 5 years old PC which, to be honest feel to be slower and slower especially when I was asking him to plot a million points on a graphic. This project made me more confident in my use of R and helped me quite a lot in learning this tool. And hopefully will help me pushing even further in the subject. I sadly did not find the time to try to do some modelisation but with the data I had this would have surely not brought that much since the few variables I had to try to predict the prices were not explaining enough of it to have something really precise.

About the results now, to be honest I was at first awaiting a bit more from the regression around the different variables. Now that I had the possibility to look at it it seems clear why they do not explain as much as I was hoping since in realty the major factors that guides the pricing of real estate are the size and built quality of them and the surrounding area is more or less the cherry on the cake. Although I still believe that those data could be used in modeling the market in a more precise way.

About limitations, the main issue of this work is the fact that we are only looking at one year, meaning that to be sure about the links we should look at them over the course of a larger period of time. This could be part of future research as well as doing for instance a more precise analysis of the crime levels ad which crimes affect the most the prices to be able to control this since crimes can go from “simple” harassment up to physical violence which would certainly not have the same effect on the prices.

Appendix

In this section I’ll show and explain the code to create the map. For this map I used some shapefiles coming from https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/England_lsoa_2011_sgen_clipped.zip we are using the full file without any reduction which gives the most precision in the output.

I first load the datas from the IMD and tidy them again, since the code is coming before the data section I need to do this otherwise there would be an issue.

#check appendix to understand the code
IMD <-
  read.csv(here::here("data/Domains_of_Deprivation_by_LSOA.csv"))
IMD <- as_tibble(IMD)
IMD <- IMD %>%
  rename(
    LSOA_code =  ï..LSOA.code..2011.,
    LSOA_name = LSOA.name..2011.,
    IMD_rank = Index.of.Multiple.Deprivation..IMD..Rank..where.1.is.most.deprived.,
    IMD_decile = Index.of.Multiple.Deprivation..IMD..Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Income_rank = Income.Rank..where.1.is.most.deprived.,
    Income_decile = Income.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Employment_rank = Employment.Rank..where.1.is.most.deprived.,
    Employment_decile = Employment.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Crime_rank = Crime.Rank..where.1.is.most.deprived.,
    Crime_decile = Crime.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Living_Environment_rank = Living.Environment.Rank..where.1.is.most.deprived.,
    Living_Environment_Decile = Living.Environment.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Health_Deprivation_rank = Health.Deprivation.and.Disability.Rank..where.1.is.most.deprived.,
    Health_Deprivation_decile = Health.Deprivation.and.Disability.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Local_Authority_Name = Local.Authority.District.name..2019.,
    Skill_rank = Education..Skills.and.Training.Rank..where.1.is.most.deprived.,
    Skill_decile = Education..Skills.and.Training.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Barrier_to_Housing_Rank = Barriers.to.Housing.and.Services.Rank..where.1.is.most.deprived.,
    Barrier_to_Housing_Decile = Barriers.to.Housing.and.Services.Decile..where.1.is.most.deprived.10..of.LSOAs.,
    Local_Authority_code = Local.Authority.District.code..2019.
  )
IMD_tidy <- IMD %>%
  select(
    LSOA_code,
    LSOA_name,
    IMD_rank,
    IMD_decile,
    Income_rank,
    Income_decile,
    Employment_rank,
    Employment_decile,
    Crime_rank,
    Crime_decile,
    Health_Deprivation_rank,
    Health_Deprivation_decile,
    Skill_rank,
    Skill_decile
  )%>%
  #deleting spaces to avoir creation of NA for the next part
  mutate(IMD_rank = gsub(" ", "", IMD_rank),
         IMD_decile = gsub(" ", "", IMD_decile),
         Income_rank = gsub(" ", "", Income_rank),
         Income_decile = gsub(" ", "", Income_decile),
         Employment_rank = gsub(" ", "", Employment_rank),
         Employment_decile = gsub(" ", "", Employment_decile),
         Crime_rank = gsub(" ", "", Crime_rank),
         Crime_decile = gsub(" ", "", Crime_decile),
         Health_Deprivation_rank = gsub(" ", "", Health_Deprivation_rank),
         Health_Deprivation_decile = gsub(" ", "", Health_Deprivation_decile),
         Skill_rank = gsub(" ", "", Skill_rank),
         Skill_decile = gsub(" ", "", Skill_decile)
  )%>%
  #transforming numbers into the right class, from character to integers
  mutate(
    IMD_rank = as.integer(IMD_rank),
    IMD_decile = as.integer(IMD_decile),
    Income_rank= as.integer(Income_rank),
    Income_decile = as.integer(Income_decile),
    Employment_rank = as.integer(Employment_rank),
    Employment_decile = as.integer(Employment_decile),
    Crime_rank = as.integer(Crime_rank),
    Crime_decile = as.integer(Crime_decile),
    Health_Deprivation_rank = as.integer(Health_Deprivation_rank),
    Health_Deprivation_decile = as.integer(Health_Deprivation_decile),
    Skill_rank = as.integer(Skill_rank),
    Skill_decile = as.integer(Skill_decile)
  )

Once the Data is tidy, I need to select only what I will truly be using therefore I am taking only the code and the IMD_decile from the table since those are the only things needed to respectively link the table and then fill the map to visualize the data.

IMD_map <- IMD_tidy %>%
  rename(code = LSOA_code)%>%
  select(code,
         IMD_decile)%>%
  mutate(code = gsub(" ", "", code))

Once the data is ready we need to use 2 new libraries, SF which is used to read and manipulate the shapefile and tmap which will be use to create the map:

library(sf)
library(tmap)

Once the libraries are loaded we will use the function st_read from the sf package to read the shapefile which will enable us to work with it:

data<-st_read(here::here("Map/england_lsoa_2011_sgen_clipped.shp"))

Now that we are able to work with the shapefile, we are going to join the IMD table that we tidied earlier on to be able to plot latter:

full_map <- left_join(data, IMD_map, by ="code")

Then we add the styling we want for the map by using tmap_style:

tmap_style("cobalt")

And finally we use the qtm function that is doing a “Quick Thematic Map Plot” for us: and we have our nice map done.

qtm(full_map, fill = "IMD_decile", border = NULL)

At first I wanted to add the tmap_mode("view") function that should make the plot interactive but it seems that it does not work with what I have done, not surre about why.